1 Description

This R notebook details the data processing and visualization for growth competition experiments with a CRISPRi sgRNA library. The library contains around 20,000 unique sgRNA repression mutants tailored for the cyanobacterium Synechocystis sp. PCC6803. This library is the second version (therefore “V2”) of an sgRNA library for Synechocystis, containing five instead of only two sgRNAs per gene. In some cases, genes or ncRNAs are so short that it is not possible to design a maximum of five individual sgRNAs.

The first iteration of the Synechocystis sgRNA library was published in Nature Communications, 2020.

2 Prerequisites

Load required packages.

suppressPackageStartupMessages({
  library(tidyverse)
  library(ggrepel)
  library(lattice)
  library(latticeExtra)
  library(latticetools)
  library(scales)
  library(dendextend)
  library(vegan)
  library(tsne)
  library(KEGGREST)
  library(limma)
  library(corrplot)
  library(kableExtra)
  library(grid)
  library(ggpubr)
})

Define global figure style, default colors, and a plot saving function.

3 Quality control

3.1 Data import

Load raw data. The main table contains already normalized quantification of all sgRNAs, fold change, multiple hypothesis corrected p-values, and fitness score. Contrary to the processing of our first CRISPRi library V1, much of the functionality from the notebook was transferred into the new CRISPRi library pipeline on github.

# load first seq run
load("../data/input/DESeq2_result.Rdata")
df_main <- DESeq_result_table

# load second seq run
load("../data/input/DESeq2_result_2.Rdata")
df_main <- bind_rows(df_main, DESeq_result_table)

# remove single results table
rm(DESeq_result_table)

3.2 Data annotation

Different annotation columns are added to the main data frame, including a short sgRNA identifier (excluding the position on the gene), an sgRNA index (1 to 5), and genome annotation from Uniprot. The Uniprot data is dynamically downloaded for every update of this pipeline using their very simple API (read_tsv("https://www.uniprot.org/uniprot/?query=taxonomy:1111708&format=tab")). The full list of columns that can be queried is available here. Pathway annotation from KEGG is later in the pipeline added using the KEGGREST package.

df_main <- df_main %>%
  # correct an error in sgRNA naming
  mutate(sgRNA = gsub('”', '2', sgRNA)) %>%
  # split sgRNA names into target gene and position
  separate(sgRNA, into = c("sgRNA_target", "sgRNA_position"), sep = "\\|",
    remove = FALSE) %>%
  
  # add sgRNA index number (1 to maximally 5) and type
  group_by(sgRNA_target) %>%
  mutate(
    sgRNA_position = as.numeric(sgRNA_position),
    sgRNA_index = sgRNA_position %>% as.factor %>% as.numeric,
    sgRNA_type = if_else(grepl("^nc_", sgRNA), "ncRNA", "gene")) %>%
  ungroup %>%
  
  # map trivial names to LocusTags using a manually curated list
  left_join(
    read_tsv("../data/input/mapping_trivial_names.tsv", col_types = cols()),
    by = c("sgRNA_target" = "gene")) %>%
  
  # remove some empty rows (NA targets)
  filter(!is.na(sgRNA_target)) %>%
  
  # remove 2 conditions without response
  filter(!condition %in% c("BG11", "LC, 200uE")) %>%
  
  # split condition into separate cols
  separate(condition, into = c("carbon", "light", "treatment_1", "treatment_2"),
    sep = ", ", remove = FALSE, fill = "right") %>%
  unite("treatment", treatment_1, treatment_2, sep = ", ", na.rm = TRUE)

Overview about the different conditions.

df_cultivation_summary <- df_main %>% group_by(condition) %>%
  summarize(
    time_points = paste(unique(time), collapse = ", "),
    carbon = unique(carbon),
    light = unique(light),
    treatment = unique(treatment),
    min_fit = min(fitness),
    med_fit = median(fitness),
    max_fit = max(fitness))

print(df_cultivation_summary)
write_csv(df_cultivation_summary, file = "../data/output/cultivation_summary.csv")

Retrieve gene info from uniprot and merge with main data frame. We need to make a custom function to retrieve and parse the data from uniprot, because of a bug in the security level on Ubuntu 20.04. The fallback option is to load a local copy of uniprot annotation for this organism.

library(httr)
uniprot_url <- paste0(
   "https://www.uniprot.org/uniprot/?query=taxonomy:1111708&format=tab&",
   "columns=id,genes,genes(PREFERRED),protein_names,length,mass,ec,database(KEGG)")

get_uniprot <- function(url) {
  # reset security level, caused by a faulty SSL certificate on server side,
  # see this thread: https://github.com/Ensembl/ensembl-rest/issues/427
  httr_config <- config(ssl_cipher_list = "DEFAULT@SECLEVEL=1")
  res <- with_config(config = httr_config, GET(url))
  server_error = simpleError("")
  df_uniprot <- tryCatch(
    read_tsv(content(res), col_types = cols()),
    error = function(server_error) {
      message("Uniprot server not available, falling back on local Uniprot DB copy")
      read_tsv("../data/input/uniprot_synechocystis.tsv", col_types = cols())
    }
  )
}

df_uniprot <- get_uniprot(uniprot_url) %>%
  rename_with(tolower) %>%
  rename(locus = `cross-reference (kegg)`, gene_name = `gene names`,
    gene_name_short = `gene names  (primary )`, ec_number = `ec number`,
    protein = `protein names`, uniprot_ID = entry
  ) %>%
  separate_rows(locus, sep = ";syn:") %>%
  mutate(locus = str_remove_all(locus, "syn:|;")) %>%
  filter(!is.na(locus))

df_main <- left_join(df_main, filter(df_uniprot, !duplicated(locus)),
  by = "locus")

3.3 Number of sgRNAs

Each gene is represented by up to five sgRNAs. We can test if all or only some of the 5 sgRNAs are “behaving” in the same way in the same conditions, more mathematically speaking we can estimate the correlation of every sgRNA with another. First let’s summarize how many genes have 5, 4, 3 sgRNAs and so on associated with them.

# N unique sgRNAs in dataset
paste0("Number of unique sgRNAs: ", unique(df_main$sgRNA) %>% length)
[1] "Number of unique sgRNAs: 21705"
# N genes with 1,2,3,4 or 5 sgRNAs
plot_sgRNAs_per_gene <- df_main %>%
  group_by(sgRNA_type, sgRNA_target) %>%
  summarize(n_sgRNAs = length(unique(sgRNA_position)), .groups = "drop_last") %>%
  count(n_sgRNAs) %>% filter(n_sgRNAs <= 5) %>%
  ggplot(aes(x = factor(n_sgRNAs, 5:1), y = n, label = n)) +
  geom_col(show.legend = FALSE) +
  geom_text(size = 3, nudge_y = 200, color = grey(0.5)) +
  facet_grid(~ sgRNA_type) +
  labs(x = "n sgRNAs / target", y = "n targets") +
  coord_cartesian(ylim = c(-50, 3500)) +
  custom_theme()

print(plot_sgRNAs_per_gene)

save_plot(plot_sgRNAs_per_gene, width = 6, height = 3.5)

4 Normalization

4.1 Fitness distribution of all conditions

Before biological analysis continues, we need to check if fitness (and log2 FC from which it is calculated) is equally distributed. For example, strictly essential genes like ribosomal genes should show the same degreee of depletion over time, regardless of condition.

We can compare fitness over all conditions using a scatter plot matrix. We can see that some conditions are very similar to each other, for example the conditions treated with glucose (LC, LL +g, LC, LL, +D, +G, HC, LL +g). Others are more dissimilar to the rest, for example LC, IL and LC, LL, +FL. They are more alike each other, although LC, LL, +FL should be more comparable to LC, LL, hinting at experimental bias. In this case both of these conditions (and LC, LL, +G) were pre-cultivated in low light instead of high light, as opposed to the rest of the samples.

df_main %>% filter(time == 0, sgRNA_index == 1) %>%
  select(locus, condition, fitness) %>%
  filter(!is.na(locus)) %>%
  pivot_wider(names_from = condition, values_from = fitness) %>%
  select(-locus) %>%
  custom_splom(pch = 19, cex = 0.3, col = grey(0.4, 0.4), pscales = 0)

4.2 Normalization strategy

In order to account for experimental or quantification bias, we can try to normalize the log2 FC distribution between all samples, and then re-calculate fitness. The underlying assumption is that e.g. essential genes should deplete at the same rate and hence show identical log2 FC at identical time points. Different types of experimental bias influence global fitness distribution and should be reduced with normalization. Here we try a ‘cyclic loess’ or quantile normalization that gave good results in a quick comparison.

# construct a normalization function that takes three colums as input,
# the numeric variable to be normalized, the conditioning variable
# (character or factor), and an ID that identifies each observation (sgRNA)
apply_norm = function(id, cond, var) {
  df_orig <- tibble(id = id, cond = cond, var = var)
  df_new <- pivot_wider(df_orig, names_from = cond, values_from = var) %>%
  column_to_rownames("id") %>% as.matrix %>%
  limma::normalizeBetweenArrays(method = "quantile") %>%
  as_tibble(rownames = "id") %>%
  pivot_longer(-id, names_to = "cond", values_to = "var_norm")
  left_join(df_orig, df_new, by = c("id", "cond")) %>% pull(var_norm)
}

# apply normalization
df_main <- df_main %>%
  mutate(FoldChange = 2^log2FoldChange) %>%
  group_by(time) %>%
  mutate(
    FoldChange_norm = apply_norm(sgRNA, condition, FoldChange),
    log2FoldChange = log2(FoldChange_norm)
  ) %>% ungroup

# compare effect of normalization
df_main %>% group_by(condition) %>% slice(1:10000) %>%
  ggplot(aes(x = log2(FoldChange), y = log2(FoldChange_norm), color = factor(time))) +
  geom_point(size = 0.5) +
  facet_wrap(~ condition, ncol = 4) +
  custom_theme() +
  scale_color_manual(values = custom_colors)

Another way to look at the result of the normalization is to compare the global distribution of log2 FC values, as a density plot.

library(ggridges)
df_main %>% filter(time == 10) %>%
  select(sgRNA, condition, FoldChange, FoldChange_norm) %>%
  pivot_longer(matches("^Fold"), names_to = "metric", values_to = "FC") %>%
  distinct %>%
  ggplot(aes(x = log2(FC), y = condition, group = condition)) + 
  geom_density_ridges(fill = "#00AFBB99", col = grey(0.4)) +
  facet_wrap(~ metric, ncol = 4) +
  lims(x = c(-2, 1.5)) +
  custom_theme()
Picking joint bandwidth of 0.0306
Picking joint bandwidth of 0.0312

Now we need to re-calculate fitness based on the normalized log2 FC.

df_main <- df_main %>%
  select(-FoldChange, -FoldChange_norm) %>%
  group_by(sgRNA, condition) %>%
  mutate(fitness = DescTools::AUC(time, log2FoldChange)/(max(time)/2)) %>%
  arrange(sgRNA_target, sgRNA_index, condition, time)
Registered S3 method overwritten by 'data.table':
  method           from
  print.data.table     

5 Fitness score aggregation

5.1 Correlation of sgRNAs

Different methods can be used to estimate similarity between samples (sgRNAs). For example, factor analysis is a method to dissect underlying sources of variation within the dataset, and the contribution to overall variation. The most famous example is principal component analysis (PCA). We can also use the correlation coefficient of sgRNAs to each other to see if one of the sgRNAs contributes stronger to overall variation.

This is an example of an apparently strictly essential gene, encoding the ribosomal protein rps10. Most of the sgRNA repressor strains are depleted, the correlation between sgRNAs is high. The strength of depletion varies though, and the strain with sgRNA 3 is not depleted at all. We want to give higher weights to sgRNAs that correlate well with each other, and/or show stronger effect (depletion/enrichment).

plot_sgRNA_ribo_example <- df_main %>% filter(sgRNA_target == "rps10") %>%
  mutate(sgRNA_index = factor(sgRNA_index, 1:5)) %>%
  ggplot(aes(x = time, y = log2FoldChange, color = sgRNA_index)) +
  geom_line(size = 1) + geom_point(size = 2) +
  facet_wrap(~ condition, ncol = 4) +
  custom_theme() +
  scale_color_manual(values = custom_range(5))

print(plot_sgRNA_ribo_example)

save_plot(plot_sgRNA_ribo_example, width = 7, height = 5.5)

A correlation score can be calculated by computing the correlation coefficient of all sgRNAs to each other. This score is robustly summarized by taking the median, and rescaling it from the respective minima and maxima [-1, 1] to [0, 1]. This score serves as a weight component for each sgRNA to calculate the (global) weighted mean of log2 FC over all sgRNAs. The score has the characteristic that it gives a weight of 1 for an sgRNA perfectly correlated with all other sgRNAs of the same gene, and a weight of 0 for sgRNAs perfectly anti-correlated to the other sgRNAs.

For a matrix of \(x = 1 .. m\) sgRNAs and \(y = 1 .. n\) observations (measurements), the correlation \(R\) of one sgRNA to another is calculated using Pearson’s method:

\(R_x=cor([log_2FC_{x1,y1} ... log_2FC_{x1,yn}], [log_2FC_{x2,y1} ... log_2FC_{x2,yn}])\)

The correlation weight of one sgRNA is then calculated as median of all \(R\) rescaled between 0 and 1.

\(w_x = \frac{1 + median(R_1, R_2, ..., R_m)}{2}\)

The following example shows the correlation matrix for the 5 rps10 sgRNAs, and their weights. The self correlation of each sgRNA (R = 1) is removed prior to weight determination.

cor_matrix <- df_main %>% filter(sgRNA_target == "rps10") %>% ungroup %>%
  select(sgRNA_index, log2FoldChange, condition, time) %>%
  pivot_wider(names_from = c("condition", "time"), values_from = log2FoldChange) %>%
  arrange(sgRNA_index) %>% column_to_rownames("sgRNA_index") %>%
  as.matrix %>% t %>% cor(method = "pearson")

weights <- cor_matrix %>% replace(., . == 1, NA) %>%
  apply(2, function(x) median(x, na.rm = TRUE)) %>%
  rescale(from = c(-1, 1), to = c(0, 1))

# plot heatmap
lattice::levelplot(cor_matrix %>% replace(., . == 1, NA),
  col.regions = custom_range(20))


# print weights
weights
        1         2         3         4         5 
0.8440521 0.7864564 0.4605635 0.8265134 0.7689177 

Now we can create a function that will compute weights for all sgRNAs, and add the weights to the data set.

determine_corr <- function(index, value, condition, time) {
  # make correlation matrix
  df <- data.frame(index = index, value = value, condition = condition, time = time)
  cor_matrix <- pivot_wider(df, names_from = c("condition", "time"), values_from = value) %>%
    arrange(index) %>% column_to_rownames("index") %>%
    as.matrix %>% t %>% cor(method = "pearson")
  
  # determine weights
  weights <- cor_matrix %>% replace(., . == 1, NA) %>%
    apply(2, function(x) median(x, na.rm = TRUE)) %>%
    scales::rescale(from = c(-1, 1), to = c(0, 1)) %>%
    enframe("index", "weight") %>% mutate(index = as.numeric(index)) %>%
    mutate(weight = replace(weight, is.na(weight), 1))
  
  # return vector of weights the same order and length 
  # as sgRNA index vector
  left_join(df, weights, by = "index") %>% pull(weight)
}

df_main <- df_main %>%
  group_by(sgRNA_target) %>%
  mutate(sgRNA_correlation = determine_corr(sgRNA_index,
    log2FoldChange, condition, time))

5.2 Efficiency of sgRNAs

The correlation of each sgRNA with each other is a “global” parameter as it is identical over all conditions. A second global parameter, sgRNA efficiency, can be obtained using a similar approach. We expect that fitness of all sgRNAs for one gene is not normally distributed because sgRNAs are not ideal replicate measurements. They are biased by position effects and off-target binding, see Wang et al., Nature Comms, 2018 for a very insightful and comprehensive analysis of the number and position of sgRNAs required to estimate gene fitness.

We calculate sgRNA efficiency \(E\) as the median absolute fitness (AUC of log2FC over time) of an sgRNA \(x = 1 .. m\) over all observations [conditions] \(y = 1 .. n\).

\(E_x=median(abs(fitness_{x1, y1}, fitness_{x1, y2}, ..., fitness_{x1, yn}))\)

To normalize between all sgRNAs, \(E\) is rescaled to a range between 0 and 1.

\(E_x=\frac{E_x}{max(E_1, E_2, ..., E_m)}\)

df_main <- df_main %>% group_by(sgRNA_target) %>%
  mutate(sgRNA_efficiency = ave(fitness, sgRNA_index, FUN = function(x) median(abs(x))) %>%
    {./max(.)})

This is the resulting sgRNA efficiency for the example gene above, rps10.

df_main %>% filter(sgRNA_target == "rps10") %>% ungroup %>%
  select(sgRNA_index, sgRNA_efficiency) %>% distinct %>% 
  arrange(sgRNA_index) %>% deframe
        1         2         3         4         5 
1.0000000 0.1519365 0.0351794 0.2105323 0.5110918 

5.3 Position bias of sgRNA repression

Plot the weight of each sgRNA to see if there is a dependency between correlation and sgRNA position. There is no significant trend.

We can also quantify how many genes have strongly correlated sgRNAs and how many have outliers. In order to do this, the median weight of the (up to) 5 sgRNAs per gene is plotted. Generally, the median weight ranges between 0.5 and 1.0, showing on average good correlation.

plot_sgRNA_correlation <- df_main %>%
  select(sgRNA_target, sgRNA_index, sgRNA_correlation) %>%
  filter(sgRNA_index <= 5) %>%
  distinct %>%
  # plot
  ggplot(aes(x = factor(sgRNA_index), y = sgRNA_correlation)) +
  geom_boxplot(outlier.shape = "") +
  labs(x = "sgRNA position", y = "correlation") +
  stat_summary(fun.data = function(x) c(y = median(x)+0.07, 
    label = round(median(x), 2)), geom = "text", size = 3) +
  stat_summary(fun.data = function(x) c(y = 1.1, 
    label = length(x)), geom = "text", color = grey(0.5), size = 3) +
  coord_cartesian(ylim = c(-0.15, 1.15)) +
  custom_theme()

plot_sgRNA_correlation_hist <- df_main %>%
  select(sgRNA_target, sgRNA_index, sgRNA_correlation) %>%
  filter(sgRNA_index <= 5) %>%
  distinct %>% group_by(sgRNA_target) %>%
  summarize(
    median_sgRNA_correlation = median(sgRNA_correlation),
    min_sgRNA_correlation = min(sgRNA_correlation)
  ) %>%
  # plot
  ggplot(aes(x = median_sgRNA_correlation)) +
  geom_histogram(bins = 40, fill = custom_colors[1], alpha = 0.7) +
  custom_theme()

save_plot(plot_sgRNA_correlation_hist, width = 5, height = 4)
save_plot(plot_sgRNA_correlation, width = 5, height = 4)
ggarrange(plot_sgRNA_correlation, plot_sgRNA_correlation_hist, ncol = 2)

Second, the binding position of the sgRNAs could be correlated to the strength of repression. In other words sgRNAs binding closer to the promoter could have stronger ability to repress a gene, see Figure 1 B in Wang et al., Nature Comms, 2018. We plot sgRNA efficiency for genes only, because the absolute majority of those has 5 sgRNAs.

plot_sgRNA_efficiency <- df_main %>%
  filter(sgRNA_index <= 5, sgRNA_type == "gene") %>%
  select(sgRNA_target, sgRNA_index, sgRNA_efficiency) %>% distinct %>%
  ggplot(aes(x = factor(sgRNA_index), y = sgRNA_efficiency)) +
  geom_boxplot(notch = FALSE, outlier.shape = ".") +
  labs(x = "sgRNA position (relative)", y = "repression efficiency") +
  coord_cartesian(ylim = c(-0.15, 1.15)) +
  stat_summary(fun.data = function(x) c(y = median(x)+0.07, 
    label = round(median(x), 2)), geom = "text", size = 3) +
  stat_summary(fun.data = function(x) c(y = 1.1, 
    label = length(x)), geom = "text", color = grey(0.5), size = 3) +
  custom_theme()


plot_sgRNA_efficiency_hist <- df_main %>%
  filter(sgRNA_index <= 5, sgRNA_type == "gene") %>%
  select(sgRNA_target, sgRNA_position, sgRNA_efficiency) %>% distinct %>%
  group_by(sgRNA_position) %>%
  summarize(sgRNA_efficiency = median(sgRNA_efficiency), n_pos = n()) %>%
  filter(n_pos >= 10) %>%
  ggplot(aes(x = sgRNA_position, y = sgRNA_efficiency)) +
  labs(x = "sgRNA position (nt)", y = "repression efficiency") +
  geom_point(col = alpha(custom_colors[5], 0.5)) +
  geom_smooth() +
  custom_theme()

save_plot(plot_sgRNA_efficiency, width = 5, height = 4)
save_plot(plot_sgRNA_efficiency_hist, width = 5, height = 4)
`geom_smooth()` using method = 'loess' and formula 'y ~ x'
`geom_smooth()` using method = 'loess' and formula 'y ~ x'
ggarrange(plot_sgRNA_efficiency, plot_sgRNA_efficiency_hist, ncol = 2)
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

Export draft Figure 1 for manuscript.

plot_selected_sgRNAs <- df_main %>%
  filter(
    grepl("ctrl[1-5]$|rps10$", sgRNA_target), 
    condition %in% c("HC, HL", "HC, LL", "LC, IL", "LC, LL")) %>%
  mutate(
    sgRNA_index2 = as.numeric(str_extract(sgRNA_target, "[1-9]$")),
    sgRNA_index = case_when(sgRNA_position == 0 ~ sgRNA_index2, TRUE ~ sgRNA_index),
    sgRNA_target = str_extract(sgRNA_target, "[a-zA-Z]*")
  ) %>%
  ggplot(aes(x = time, y = log2FoldChange, color = factor(sgRNA_index))) +
  geom_line(size = 1) + geom_point(size = 2) +
  facet_grid(sgRNA_target ~ condition) +
  custom_theme(legend.position = 0) +
  coord_cartesian(ylim = c(-4.5, 2.5)) +
  scale_color_manual(values = custom_range(5))

svg(filename = "../figures/figure1.svg", width = 7, height = 5.5)
ggarrange(ncol = 2, nrow = 2, widths = c(0.6, 0.4), labels = LETTERS[1:4], font.label = list_fontpars,
  plot_sgRNAs_per_gene + theme(plot.margin = unit(c(12,12,12,12), "points")),
  plot_sgRNA_efficiency + theme(plot.margin = unit(c(26,12,12,12), "points")),
  plot_selected_sgRNAs + theme(plot.margin = unit(c(12,-4,12,14), "points")),
  plot_sgRNA_correlation + theme(plot.margin = unit(c(26,12,12,12), "points"))
)
dev.off()
null device 
          1 

Export supplemental figure with all ribosomal genes (rpsNN/rplNN).

plot_sgRNAs_ribosome <- df_main %>%
  filter(str_detect(sgRNA_target, "rp[sl][0-9]*$")) %>%
  filter(condition == "LC, LL") %>%
  ggplot(aes(x = time, y = log2FoldChange, color = factor(sgRNA_index))) +
  geom_line(size = 1) + geom_point(size = 2) +
  facet_wrap(~ sgRNA_target, ncol = 7) +
  custom_theme(legend.position = "top") +
  scale_color_manual(values = custom_range(5))

print(plot_sgRNAs_ribosome)

6 Gene fitness calculation

6.1 Summarize sgRNA fitness to gene fitness

With the correlation and the efficiency per sgRNA, we can compute the weighted mean of all sgRNAs. For comparison, we also test simple strategies such as the standard arithmetic mean and a top 1 and top 2 sgRNAs strategy. Metrics are calculated for log2 FC, and fitness.

df_controls <- df_main %>% ungroup %>% 
  filter(str_detect(sgRNA_target, "ctrl[0-9]+$"))

df_gene <- df_main %>%
  
  # keep all annotation columns
  group_by(sgRNA_target, sgRNA_type, locus, gene_name, condition, 
    carbon, light, treatment, time) %>%
  
  # summarize FC and fitness...
  summarize(.groups = "drop",
    
    # log2 FC
    mean_log2FoldChange = mean(log2FoldChange),
    wmean_log2FoldChange = weighted.mean(log2FoldChange, sgRNA_correlation * sgRNA_efficiency),
    top1_log2FoldChange = log2FoldChange[which.max(sgRNA_efficiency)],
    top2_log2FoldChange = mean(log2FoldChange[order(sgRNA_efficiency, decreasing = TRUE)[1:2]]),
    sd_log2FoldChange = sd(log2FoldChange),
    
    # fitness
    mean_fitness = mean(fitness),
    wmean_fitness = weighted.mean(fitness, sgRNA_correlation * sgRNA_efficiency),
    top1_fitness = fitness[which.max(sgRNA_efficiency)],
    top2_fitness = mean(fitness[order(sgRNA_efficiency, decreasing = TRUE)[1:2]]),
    sd_fitness = sd(fitness),
    
    # apply significance test, Mann-Whitney U test
    p_value = wilcox.test(fitness, filter(df_controls, condition == unique(condition))$fitness)$p.value
  )

Since statistical significance is tested for many genes in parallel, the p-value obtained from MWU test should be multiple-hypothesis corrected. For this purpose we use the Benjamini-Hochberg method. We also calculate a score taking both effect size and p-value into account, according to the publication from Wang et al., Nat Comm, 2018. This score is simply the absolute fitness score multiplied by the negative log10 p-value.

df_gene <- df_gene %>%
  group_by(condition, time) %>%
  mutate(
    p_value_adj = p.adjust(p_value, method = "BH"),
    score = abs(wmean_fitness)*-log10(p_value_adj)
  ) %>% ungroup

A comparison of log2 FC aggregated by the different method shows clear differences. For the example gene rps10 the weighted mean and the top method give similar results, representative of the stronger influence from highly depleted sgRNA repression strains. The regular mean is robust, but “shallow”, probably underestimating the real effect n fitness. The top 1 method simply picks the most depleted/enriched sgRNA (over all conditions) as representative.

df_gene %>% filter(sgRNA_target == "rps10") %>%
  pivot_longer(cols = matches("[n12]_log2FoldChange"), 
    names_to = "metric", values_to = "log2FoldChange") %>%
  mutate(metric = str_remove(metric, "_log2FoldChange")) %>%
  ggplot(aes(x = time, y = log2FoldChange, 
    ymin = log2FoldChange-sd_log2FoldChange, 
    ymax = log2FoldChange+sd_log2FoldChange, color = fct_inorder(metric))) +
  geom_line(size = 1) + geom_point(size = 2) + geom_linerange(size = 1) +
  facet_wrap(~ condition, ncol = 4) +
  custom_theme(legend.position = "right") +
  coord_cartesian(ylim = c(-3.75, 0.75)) +
  scale_color_manual(values = custom_range(4))

This plot shows a comparison of the 4 methods for the first 36 genes by alphabetical order, for one selected condition only (1% CO2, BG11, 1,000 µmol photons m-1 s-1). Here we can see that the top1 method is often but not always representative for the gene: For apcD or apcF, it does not seem representative compared to the mean, weighted mean, and top2 methods.

df_gene %>% filter(
    gene_name %in% unique(.data[["gene_name"]])[1:36],
    condition == "HC, HL"
  ) %>%
  pivot_longer(cols = matches("[n12]_log2FoldChange"), names_to = "metric", values_to = "log2FoldChange") %>%
  mutate(metric = str_remove(metric, "_log2FoldChange")) %>%
  ggplot(aes(x = time, y = log2FoldChange, 
    ymin = log2FoldChange-sd_log2FoldChange,
    ymax = log2FoldChange+sd_log2FoldChange, color = fct_inorder(metric))) +
  geom_line(size = 1) + geom_point(size = 2) + geom_linerange(size = 1) +
  facet_wrap(~ sgRNA_target, ncol = 7) +
  custom_theme(legend.position = "top") +
  coord_cartesian(ylim = c(-5, 5)) +
  scale_color_manual(values = custom_range(4))

6.2 Global distribution of gene fitness

Global distribution of weighted mean fitness for all genes. Effect of ncRNA repression seems to be much lower than effect of gene repression.

plot_all_fitness_hist <- df_gene %>% filter(time == 0) %>%
  ggplot(aes(x = wmean_fitness, fill = sgRNA_type)) +
  geom_histogram(bins = 100) +
  coord_cartesian(xlim = c(-4, 4), ylim = c(0, 1000)) +
  facet_wrap( ~ condition, ncol = 4) +
  custom_theme() +
  scale_fill_manual(values = custom_colors[c(3:4)])

print(plot_all_fitness_hist)

save_plot(plot_all_fitness_hist, width = 7, height = 5)

6.3 Gene fitness vs significance

plot_all_fitness_volc <- df_gene %>% filter(time == 0,
    condition %in% c("HC, HL", "LC, LL")) %>%
  arrange(sgRNA_type) %>%
  ggplot(aes(x = wmean_fitness, y = -log10(p_value_adj), col = sgRNA_type)) +
  geom_point(alpha = 0.5, size = 0.5) +
  geom_line(data = data.frame(x = c(seq(-8, -0.5, 0.1), seq(0.5, 8, 0.1)),
    y = 4/c(seq(8, 0.5, -0.1), seq(0.5, 8, 0.1))),
    aes(x = x, y = y, shape = NULL, col = NULL), lty = 2) +
  coord_cartesian(xlim = c(-7, 7), ylim = c(0, 4)) +
  custom_theme(aspect = 1, legend.position = "left", legend.key.size = unit(0.4, "cm")) +
  facet_wrap(~ condition) +
  labs(x = "fitness", y = expression("-log"[10]*" p-value")) +
  scale_color_manual(values = custom_colors[3:4]) +
  scale_shape_manual(values=c(1, 19))

print(plot_all_fitness_volc)

save_plot(plot_all_fitness_volc, width = 6, height = 3)

6.4 Behavior of control sgRNAs

Ten sgRNAs were included in the library that have no gene-specific targets. The following plot shows that these negative controls do not have an effect on strain fitness, except probably 2 sgRNAs in one specific condition.

plot_controls_sgRNAs <- df_main %>% filter(grepl("ctrl", sgRNA_target)) %>%
  ggplot(aes(x = time, y = log2FoldChange, color = sgRNA_target)) +
  geom_line(size = 1) + geom_point(size = 2) + ylim(-5, 5) +
  facet_wrap(~ condition, ncol = 4) +
  custom_theme() +
  scale_color_manual(values = custom_range(10))

print(plot_controls_sgRNAs)

save_plot(plot_controls_sgRNAs, width = 7, height = 5.5)

7 Gene enrichment

To plot gene fitness for the enzymes of central carbon metabolism, we need a complete list of enzymes and the genes that they are mapped to. To list the different KEGG databases that can be queried, use listDatabases(). Gene-pathway mappings are obtained and merged with pathway names and gene/enzyme names.

# get mapping of pathways for each gene
df_kegg <- keggLink("pathway", "syn") %>%
  enframe(name = "locus", value = "kegg_pathway_id") %>%
  
  # get list of pathways with name/ID pairs
  left_join(by = "kegg_pathway_id",
    keggList("pathway", "syn") %>%
    enframe(name = "kegg_pathway_id", value = "kegg_pathway")
  ) %>%
  
  # get list of gene/enzyme names
  left_join(by = "locus",
    keggList("syn") %>%
    enframe(name = "locus", value = "kegg_gene") %>%
    mutate(kegg_gene_short = str_extract(kegg_gene, "^[a-zA-Z0-9]*;") %>% 
      str_remove(";"))
  ) %>%
  
  # trim useless prefixes
  mutate(
    locus = str_remove(locus, "syn:"),
    kegg_pathway_id = str_remove(kegg_pathway_id, "path:"),
    kegg_pathway = str_remove(kegg_pathway, " - Synechocystis sp. PCC 6803")
  )

head(df_kegg)

7.1 Fitness per pathway

Sometimes even small effects in fitness can be relevant if several genes of the same pathway (or iso-enzymes) are affected. A simple fitness threshold will not reveal those changes. In such cases a more nuanced approach can be taken, a gene set enrichment analysis (GSEA). Several packages exist to test if functionally related genes are enriched, depleted, or both at the same time / the same conditions.

Before we test for enrichment of associated pathways/GO terms, we can have a look at the general depletion/enrichment per KEGG pathway. The fitness distribution per pathway can be visualized using a violin- or scatter plot.

plot_median_fitness_kegg <- df_gene %>% filter(time == 0) %>%
  inner_join(df_kegg, by = "locus") %>%
  group_by(kegg_pathway, condition) %>%
  summarize(.groups = "drop",
    fitness = median(wmean_fitness),
    n_genes = n()
  ) %>% filter(n_genes >= 20) %>%
  mutate(kegg_pathway = paste0(str_sub(kegg_pathway, 1, 25), "..")) %>%
  mutate(kegg_pathway = fct_reorder(kegg_pathway, fitness, .desc = TRUE)) %>%
  
  ggplot(aes(x = fitness, y = kegg_pathway)) +
  geom_boxplot(outlier.shape = NULL, color = grey(0.5), fill = grey(0.9)) +
  geom_point(aes(color = condition)) +
  geom_vline(xintercept = 0, lty = 2, color = grey(0.5)) +
  labs(x = "median fitness", y = "") +
  custom_theme(legend.position = c(0.25, 0.25), legend.key.size = unit(0.4, "cm")) +
  scale_fill_manual(values = custom_range(11)) +
  scale_color_manual(values = custom_range(11))

print(plot_median_fitness_kegg)

Export draft Figure 2 for manuscript. We add photosystem I and II genes as examples for differential depletion. A heatmap.

plot_sgRNAs_ps1 <- df_gene %>%
  filter(str_detect(sgRNA_target, "psa[A-Z]*"), time == 0) %>%
  mutate(wmean_fitness = wmean_fitness %>% replace(., . > 4, 4) %>% replace(., . < -4, -4)) %>%
  ggplot(aes(x = condition, y = fct_rev(sgRNA_target), fill = wmean_fitness)) +
  geom_tile() + custom_theme() +
  labs(title = "Photosystem I", x = "", y = "") +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1)) +
  scale_fill_gradientn(colours = c(custom_colors[1], grey(0.9), custom_colors[2]),
    limits = c(-4, 4))

plot_sgRNAs_ps2 <- df_gene %>%
  filter(str_detect(sgRNA_target, "psb[A-Z]*"), time == 0) %>%
  mutate(wmean_fitness = wmean_fitness %>% replace(., . > 4, 4) %>% replace(., . < -4, -4)) %>%
  mutate(sgRNA_target = str_replace(sgRNA_target, "psb13", "psbW")) %>%
  ggplot(aes(x = condition, y = fct_rev(sgRNA_target), fill = wmean_fitness)) +
  geom_tile() + custom_theme() +
  labs(title = "Photosystem II", x = "", y = "") +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1)) +
  scale_fill_gradientn(colours = c(custom_colors[1], grey(0.9), custom_colors[2]),
    limits = c(-4, 4))

ggarrange(ncol = 2, plot_sgRNAs_ps1, plot_sgRNAs_ps2)

svg(filename = "../figures/figure2.svg", width = 8, height = 7)
ggarrange(ncol = 2, widths = c(0.65, 0.35),
  ggarrange(nrow = 2, heights =  c(0.34, 0.66), labels = LETTERS[1:2], font.label = list_fontpars,
    plot_all_fitness_volc + theme(plot.margin = unit(c(14,-8,14,40), "points")),
    plot_median_fitness_kegg + theme(plot.margin = unit(c(6,12,12,12), "points"))),
  ggarrange(nrow = 2, heights =  c(0.4, 0.6), labels = LETTERS[3:4], font.label = list_fontpars,
    plot_sgRNAs_ps1 + theme(plot.margin = unit(c(12,0,-14,0), "points")),
    plot_sgRNAs_ps2 + theme(plot.margin = unit(c(12,0,0,0), "points"))
  )
)
dev.off()
null device 
          1 

7.2 Gene enrichment analysis (KEGG)

We use the functions kegga for KEGG enrichment analysis and goana for GO term enrichment from the limma package. Both functions test for over or under-representation of genes associated with certain pathways or GO terms. The functions don’t take the strength of differential fitness into account (DF; the depletion/enrichment over time).

df_kegg_enrichment <- lapply(unique(df_gene$condition), function(cond) {
  df_gene %>% filter(
  sgRNA_type == "gene", time == 0,
  condition == cond) %>%
  
  # filter for differential fitness (DF) genes
  filter(!between(wmean_fitness, -2.0, 2.0), !is.na(locus)) %>%
  
  # perform KEGG enrichment
  pull(locus) %>% kegga(species.KEGG = "syn") %>%
  mutate(condition = cond)
}) %>% bind_rows

head(df_kegg_enrichment)

Now we visualize the pathways that are most enriched for DF genes. It turns out that ribosomal proteins are extremely depleted and therefore score high on the negative log10 p-value for pathway enrichment.

df_kegg_enrichment %>%
  rename(kegg_pathway = Pathway) %>%
  group_by(kegg_pathway) %>% filter(N >= 20) %>%
  select(kegg_pathway, condition, P.DE) %>%
  mutate(log10_p_value = -log10(P.DE), .keep = "unused") %>%
  mutate(kegg_pathway = paste0(str_sub(kegg_pathway, 1, 25), "..")) %>%
  
  # make correlation plot
  pivot_wider(names_from = condition, values_from = log10_p_value) %>%
  column_to_rownames(var = "kegg_pathway") %>% as.matrix %>%
  corrplot(is.corr = FALSE, tl.col = grey(0.5), tl.cex = 0.8,
    col = colorRampPalette(custom_colors[c(1,5,2)])(10), col.lim = c(0, 20))

8 Unsupervised clustering of genes

8.1 Cluster genes by similarity

We can devise a generalized tidyverse friendly function to cluster a name variable by a value, grouped by one or more grouping variables. For example, cluster genes (name) by fitness (value) over several conditions (groups). The output is a factor with re-ordered levels.

fct_cluster <- function(variable, group, value, method = "ward.D2") {
  df <- tibble(variable = variable, group = group, value = value)
  df <- pivot_wider(df, names_from = group, values_from = value)
  mat <- as.matrix(column_to_rownames(df, var = "variable"))
  cl <- hclust(dist(mat), method = method)
  ord <- order.dendrogram(as.dendrogram(cl))
  factor(variable, unique(variable)[ord])
}

Heat map of fitness for all genes and all conditions.

plot_heatmap_all <- df_gene %>% filter(time == 0, !is.na(locus)) %>%
  mutate(locus = fct_cluster(locus, condition, wmean_fitness)) %>%
  mutate(wmean_fitness = wmean_fitness %>% replace(., . > 4, 4) %>% replace(., . < -4, -4)) %>%
  ggplot(aes(x = locus, y = condition, fill = wmean_fitness)) +
  geom_tile() + custom_theme(legend.pos = "right") +
  labs(x = paste0("genes (", length(unique(df_gene$locus)),")"), y = "") +
  theme(axis.text.x = element_blank(), axis.ticks.x = element_blank()) +
  scale_fill_gradientn(colours = c(custom_colors[1], grey(0.9), custom_colors[2]),
    limits = c(-4, 4))

print(plot_heatmap_all)

save_plot(plot_heatmap_all, width = 8, height = 2.2)

Now we can plot all genes, a subset with only significant genes, and a dendrogram for clustering. The result is hard to interpret. With some exceptions, most genes are grouped in broad unspecific clusters that do not reveal clear relationships between treatment variables and fitness outcome.

# prepare new df and plot heatmap
df_heatmap <- df_gene %>% filter(time == 0, !is.na(locus)) %>%
  group_by(locus) %>% filter(any(!between(wmean_fitness, -4, 4))) %>% ungroup %>%
  mutate(locus = fct_cluster(locus, condition, wmean_fitness)) %>%
  mutate(wmean_fitness = wmean_fitness %>% replace(., . > 8, 8) %>% replace(., . < -8, -8))

plot_heatmap_sig <- df_heatmap %>%
  ggplot(aes(x = locus, y = condition, fill = wmean_fitness)) +
  geom_tile() + custom_theme(legend.pos = "right") +
  labs(x = paste0("genes (", length(unique(df_gene$locus)),")"), y = "") +
  theme(axis.text.x = element_blank(), axis.ticks.x = element_blank()) +
  scale_fill_gradientn(colours = c(custom_colors[1], grey(0.9), custom_colors[2]),
    limits = c(-8, 8))

# prepare dist object for clustering and plot dend
dist_heatmap <- df_heatmap %>% select(locus, condition, wmean_fitness) %>%
  pivot_wider(names_from = condition, values_from = wmean_fitness) %>%
  column_to_rownames(var = "locus") %>% as.matrix %>%
  dist

plot_cluster_dend <- dist_heatmap %>%
  hclust(method = "ward.D2") %>% as.dendrogram %>%
  set("branches_k_col", custom_colors[1:5], k = 5) %>%
  set("branches_lwd", 0.5) %>%
  as.ggdend %>%
  ggplot(labels = FALSE)

# arrange both on same plot
ggarrange(nrow = 2, heights =  c(0.5, 0.5),
  plot_cluster_dend + theme(plot.margin = unit(c(0.1, 0.09, -0.15, 0.136),"npc")),
  plot_heatmap_sig
)

8.2 Gene similarity by dimensionality reduction methods

We use two different dimensionality reduction methods, nMDS and t-SNE. We can check if these methods reproduce the clustering for the significantly regulated genes produced with hclust. Analysis shows that the small clusters are more strongly separated from the rest.

# set a seed to obtain same pattern for stochastic methods
set.seed(123)

# run nMDS analysis
NMDS <-  dist_heatmap %>% metaMDS
Run 0 stress 0.08477877 
Run 1 stress 0.08485663 
... Procrustes: rmse 0.002551692  max resid 0.04275329 
Run 2 stress 0.08484567 
... Procrustes: rmse 0.001445249  max resid 0.01754732 
Run 3 stress 0.09041578 
Run 4 stress 0.09773472 
Run 5 stress 0.09766743 
Run 6 stress 0.09766712 
Run 7 stress 0.09766695 
Run 8 stress 0.097774 
Run 9 stress 0.08581093 
Run 10 stress 0.08477915 
... Procrustes: rmse 0.0001249276  max resid 0.00204171 
... Similar to previous best
Run 11 stress 0.09034496 
Run 12 stress 0.08477945 
... Procrustes: rmse 0.0001738428  max resid 0.002843804 
... Similar to previous best
Run 13 stress 0.08477977 
... Procrustes: rmse 0.0002175779  max resid 0.003564318 
... Similar to previous best
Run 14 stress 0.08477903 
... Procrustes: rmse 0.0001075038  max resid 0.001756538 
... Similar to previous best
Run 15 stress 0.08484495 
... Procrustes: rmse 0.001423391  max resid 0.01739739 
Run 16 stress 0.08485667 
... Procrustes: rmse 0.002551594  max resid 0.0427536 
Run 17 stress 0.08485605 
... Procrustes: rmse 0.002540429  max resid 0.04264689 
Run 18 stress 0.08477984 
... Procrustes: rmse 0.0002060804  max resid 0.003370265 
... Similar to previous best
Run 19 stress 0.09041548 
Run 20 stress 0.09286828 
*** Solution reached
df_nmds <- NMDS$points %>% as_tibble(rownames = "locus") %>%
  left_join(enframe(name = "locus", value = "cluster",
    cutreeord(hclust(dist_heatmap, method = "ward.D2"), k = 5)))
Joining, by = "locus"
# run t-SNE analysis
SNE <- dist_heatmap %>% tsne(max_iter = 500, perplexity = 8)
sigma summary: Min. : 0.317869467815397 |1st Qu. : 0.457314965951253 |Median : 0.518197389451857 |Mean : 0.550439569803973 |3rd Qu. : 0.594385229072092 |Max. : 1.44274730511067 |
Epoch: Iteration #100 error is: 15.9553533361405
Epoch: Iteration #200 error is: 0.678497037505085
Epoch: Iteration #300 error is: 0.620797600171921
Epoch: Iteration #400 error is: 0.601017031027447
Epoch: Iteration #500 error is: 0.595616760156116
df_tsne <- SNE %>% setNames(c("x", "y")) %>% as_tibble %>%
  mutate(locus = unique(df_heatmap$locus)) %>%
  left_join(enframe(name = "locus", value = "cluster",
    cutreeord(hclust(dist_heatmap, method = "ward.D2"), k = 5)))
Joining, by = "locus"
plot_nmds <- df_nmds %>%
  ggplot(aes(x = MDS1, y = MDS2, color = factor(cluster))) +
  geom_point(size = 2) + labs(title = "nMDS") +
  custom_theme(legend.position = c(0.85, 0.78)) +
  scale_color_manual(values = custom_colors)

plot_tsne <- df_tsne %>%
  ggplot(aes(x = V1, y = V2, color = factor(cluster))) +
  geom_point(size = 2) + labs(title = "t-SNE") +
  custom_theme(legend.position = c(0.85, 0.78)) +
  scale_color_manual(values = custom_colors)

ggarrange(ncol = 2, plot_nmds, plot_tsne)

ggsave("../figures/plot_nmds_tsne.svg",
  plot = ggarrange(ncol = 2, plot_nmds, plot_tsne),
  device = "svg", width = 8, height = 4)

8.3 Fit multiple linear regression models

We can find clusters of genes with similar fitness, but it is also important to identify why they cluster together. In order to find out which variables determine the fitness outcome of a gene, we can perform multiple linear regression. Each gene needs to have fitness outcomes annotated with the different (mixed) variables carbon, light, treatment. The latter can be subdivided in individual treatment columns glucose, DCMU, fluctuating light, and so on. Multiple linear regression fits a linear model of the following form to the data:

response ~ intercept + predictor A x slope A + predictor B x slope B x ...

Here, fitness is the response variable, the different conditions are the predictors. It is important to convert the categorical predictors into (numerical) dummy variables. Then for each individual gene, multiple linear models are fitted and the power of each predictor variable to predict the response is extracted.

# fixed model with 6 predictor variables -- dynamic layout would 
# be better in future
fit_linreg <- function(y, x1, x2, x3, x4, x5, x6){
  fit <- lm(y ~ x1 + x2 + x3 + x4 + x5 + x6)
  c(coefficients(fit), summary(fit)$coefficients[, 4],
    summary(fit)$r.squared)
}

# recode categorical to numerical (dummy) variables
df_linreg <- df_gene %>%
  filter(!is.na(locus)) %>%
  select(locus, carbon, light, treatment, wmean_fitness) %>% distinct %>%
  mutate(
    carbon = recode(carbon, `HC` = 1, `LC` = 0),
    light = recode(light, `LL` = 0, `IL` = 0.5, `HL` = 1)) %>%
  mutate(dummy = 1, treatment = replace(treatment, treatment == "", "-")) %>%
  pivot_wider(names_from = treatment, values_from = dummy, values_fill = 0) %>%
  mutate(`+G` = `+G` + `+D, +G`) %>% rename(`+D` = `+D, +G`) %>% select(-`-`) %>%
  # fit model
  group_by(locus) %>%
  summarize(coefficient = fit_linreg(wmean_fitness, carbon, light, `-N`, `+FL`, `+G`, `+D`),
    .groups = "keep") %>% #unnest(coefficient) %>%
  mutate(treatment = c(rep(c("intercept", "carbon", "light", "-N", "+FL", "+G", "+D"), 2) %>% 
    paste0(rep(c("", "pval_"), each = 7), .), "r_squared"))

Now we can overlay the information of the best predictor variable on the cluster map produced by tSNE, for example, and this way identify groups of genes regulated in a similar degree, by similar variables.

plot_tsne_linreg <- df_tsne %>%
  inner_join(df_linreg, by = "locus") %>%
  left_join(select(df_gene, locus, sgRNA_target) %>% distinct, by = "locus") %>%
  filter(!str_detect(treatment, "intercept|pval|r_squared")) %>%
  mutate(sgRNA_target = if_else(abs(coefficient) > 2, sgRNA_target, "")) %>%
  mutate(point_size = abs(coefficient),
    coefficient = coefficient %>% replace(., . > 5, 5) %>% replace(., . < -5, -5)) %>%
  
  ggplot(aes(x = V1, y = V2, size = point_size,
    color = coefficient, label = sgRNA_target)) +
  geom_point() +
  labs(title = "t-SNE clustering of DF genes", 
    subtitle = paste0("dot color/size encodes effect of variable, n = ", nrow(df_tsne))) +
  custom_theme(aspect = 1) +
  scale_color_gradientn(limits = c(-5, 5),
    colours = c(custom_colors[1], grey(0.6, 0.8), custom_colors[2])) +
  scale_size_continuous(range = c(1, 6)) +
  geom_text_repel(size = 3, max.overlaps = 50) +
  facet_wrap( ~ treatment, ncol = 2)

print(plot_tsne_linreg)

This strategy reveals a list of interesting condition-specific genes:

  • Nitrogen limitation: ssr3532 - unknown short protein, strongest known interaction in STRING with GlsA glutaminase
  • Fluctuating light:
    • sll1521 - Putative diflavin flavoprotein A3 (dfa3), negatively corr. with fitness
    • sll0217 Putative diflavin flavoprotein A2 (dfa2), positively corr. with fitness
  • Mixotrophy:
    • sll0593 - glk, glucokinase, catalyzes P-ylation of Glc to G6P
    • sll1533 - pilT, fimbria assembly, mobility, Glc transport or sensing?
    • ssl3364 - unknown short protein, strongly interacts with RbcX, RbcR, Prk. Important for C-metabolism adapation?
  • Light:
    • ssr2142 ycf19, short unknown protein, interacts with psbO and Tat membrane protein insertion system,
    • slr0963 sir, sulfite reductase, ferredoxin H2O + HS + ferredoxin <-> H+ + reduced ferredoxin + sulfite, strongly interacts with other proteins in sulfur metabolism, specifically related to cofactor biosynthesis, cobalamin (vitamin B12) and siroheme
  • Light, mixotrophy, heterotrophy: cluster of photosynthesis related genes increase fitness when KOed: apcA,D,E, psbB,C,D
  • Carbon:
    • sll0217 Putative diflavin flavoprotein A2 (dfa2), KO negatively correlated with fitness with C, positive with +FL
    • sll0218 same behavior as dfa2, interacts with dfa2,4, contributes to PSII stabilization, Bersanini et al., 2017

8.4 List of genes with strong fitness correlation

The table with linear regression coefficients and p-values is reshaped to long format for better readability. The kableExtra package is used to color cells for easier recognition. Then we subset the table for each treatment in order to spot the most interesting genes.

df_linreg_wide <- df_linreg %>%
  pivot_wider(names_from = treatment, values_from = coefficient) %>%
  left_join(select(df_gene, locus, sgRNA_target) %>% distinct, by = "locus") %>%
  select(-matches("intercept")) %>%
  filter(if_any(matches("^(carb|light|\\-|\\+)"), ~ abs(.) > 2)) %>%
  mutate(across(matches("carb|light|\\-|\\+"), ~ round(., 3))) %>% 
  ungroup %>% select(sgRNA_target, locus, matches("."))

color_table <- function(df, variable) {
  filter(df, abs(.data[[variable]]) > 2) %>% 
  select(matches("^(sg|loc|r_s|carb|light|\\-|\\+)") | all_of(paste0("pval_", variable))) %>%
  arrange(desc(.data[[variable]])) %>%
  mutate(across(3:8, ~ cell_spec(., "html", color = "white",
      background = spec_color(., option = "E", scale = c(-5.5, 5.5)),
      bold = TRUE))) %>%
  kbl(format = "html", escape = F) %>%
  kable_paper("striped", full_width = F)
}
df_linreg_wide %>% color_table("carbon")
sgRNA_target locus carbon light -N +FL +G +D r_squared pval_carbon
sll0364 sll0364 2.834 -2.812 -0.108 1.758 0.601 3.578 0.8717770 0.024
slr1095 slr1095 2.429 -2.1 -1.284 -1.592 -0.274 1.777 0.6370579 0.083
sll1734 sll1734 2.426 -1.267 -0.478 -1.625 0.837 1.183 0.6560893 0.099
slr0211 slr0211 2.354 0.469 -0.044 -0.362 0.786 1.884 0.8217569 0.022
ftsZ sll1633 2.34 -2.314 -1.677 -1.439 0.318 2.136 0.9249829 0.006
ndhD3 sll1733 2.326 -1.213 -0.489 -1.392 0.67 1.172 0.6640536 0.085
ssr3532 ssr3532 2.303 -1.661 -3.733 -1.464 0.702 1.64 0.6071408 0.163
ndhF2 sll1732 2.163 -0.874 -0.602 -1.431 1.022 1.124 0.6649520 0.103
slr1818 slr1818 2.159 -1.144 -1.369 -1.55 -0.087 1.675 0.5831706 0.116
sll0488 sll0488 2.151 -1.322 -1.418 -1.434 0.047 1.624 0.6312515 0.090
sll0481 sll0481 2.141 -3.371 -1.601 -1.335 0.485 1.056 0.9686290 0.002
sll0995 sll0995 2.101 -0.278 -1.933 -1.322 0.722 1.384 0.6077491 0.132
cmpB slr0041 2.041 -1.904 -0.766 -1.032 0.133 1.15 0.5577051 0.126
sir slr0963 -2.034 4.169 1.41 1.489 -0.345 -0.303 0.8364473 0.048
rpl24 sll1807 -2.081 0.994 0.573 1.814 0.023 -1.225 0.4871941 0.200
rps9 sll1822 -2.087 0.874 0.64 1.319 -0.719 -0.988 0.5755515 0.137
slr6107 slr6107 -2.108 1.215 1.13 1.151 -0.237 -1.246 0.5493324 0.124
slr0272 slr0272 -2.122 1.596 1.27 1.27 -1.038 -0.791 0.6159525 0.127
leuD sll1444 -2.209 2.505 -0.124 1.64 -1.451 -0.038 0.6714334 0.146
rps17 ssl3437 -2.241 0.79 1.027 1.922 -0.148 -1.382 0.5117852 0.184
slr0007 slr0007 -2.308 1.932 0.695 2.012 -0.851 -1.068 0.6792795 0.109
slr1938 slr1938 -2.311 0.268 0.592 1.075 -0.772 -1.967 0.7489109 0.051
rpl35 ssl1426 -2.361 1.851 1.677 1.293 -0.482 -1.844 0.5899106 0.124
sll0218 sll0218 -2.376 1.766 0.727 1.766 -0.193 -1.1 0.6105884 0.110
sll0217 sll0217 -2.418 1.723 0.954 2.228 -0.286 -1.152 0.6281035 0.118
sll0933 sll0933 -2.634 1.271 0.081 2.115 -0.633 -0.702 0.6558952 0.105
slr1245 slr1245 -2.694 1.826 1.245 -0.805 1.092 -3.224 0.8325233 0.019
rps15 ssl1784 -2.769 1.647 1.312 2.437 -0.758 -1.499 0.7223751 0.075
df_linreg_wide %>% color_table("light")
sgRNA_target locus carbon light -N +FL +G +D r_squared pval_light
apcE slr0335 -0.681 5.071 0.314 1.093 3.962 1.65 0.8255851 0.055
apcA slr2067 0.027 4.441 0.576 0.809 3.301 2.922 0.7380667 0.127
sll1878 sll1878 -1.585 4.291 2.016 2.165 1.301 -0.213 0.7988744 0.024
sir slr0963 -2.034 4.169 1.41 1.489 -0.345 -0.303 0.8364473 0.031
cpcB sll1577 0.12 4.032 0.141 0.74 2.544 2.476 0.8276584 0.053
murC slr1423 0.232 3.875 0.345 0.027 1.164 -0.248 0.6419118 0.096
sll1378 sll1378 -0.955 3.701 -0.4 1.2 2.782 0.937 0.9545533 0.006
sll1879 sll1879 -0.306 3.662 -0.894 -0.217 0.681 -0.74 0.7312512 0.074
hitB slr0327 -1.726 3.606 1.56 1.726 0.576 -0.39 0.6687398 0.087
cpcA sll1578 0.11 3.57 0.261 0.609 2.255 2.162 0.8434275 0.044
slr0947 slr0947 0.18 3.422 0.454 0.742 1.52 0.613 0.7928550 0.028
slr1990 slr1990 -0.768 3.219 0.197 0.994 2.472 2.429 0.8920252 0.044
slr1102 slr1102 0.056 3.162 -0.118 0.511 2.737 0.819 0.8207709 0.065
amiC slr0447 -1.235 3.159 0.549 -0.175 1.309 0.402 0.9300166 0.007
sll6055 sll6055 -1.122 3.123 0.558 0.514 2.406 2.128 0.8376624 0.091
cyp2 slr0574 -0.6 3.073 0.867 1.027 2.347 -0.438 0.9444943 0.003
sll1945 sll1945 -0.194 3.072 0.642 0.625 0.063 1.299 0.7068087 0.059
sll0689 sll0689 -0.125 3.062 0.19 0.41 0.027 0.162 0.5002592 0.178
narB sll1454 -0.223 3.011 0.044 0.014 0.034 0.634 0.8309629 0.025
def slr1549 0.929 2.973 1.091 -0.039 1.821 0.97 0.7394573 0.103
slr7096 slr7096 -0.452 2.972 -0.83 0.696 -0.16 0.433 0.9130593 0.010
psbJ smr0008 0.192 2.968 0.293 0.419 3.216 3.222 0.8923081 0.090
slr1505 slr1505 -0.682 2.926 0.456 0.864 2.222 3.068 0.9263147 0.032
slr0483 slr0483 0.315 2.922 0.533 0.633 1.047 0.53 0.9363725 0.003
nirA slr0898 -1.083 2.919 -0.106 0.515 -0.343 -0.004 0.7628800 0.063
apcB slr1986 0.627 2.876 0.319 0.432 2.241 2.188 0.7619664 0.129
slr0734 slr0734 0.52 2.819 0.607 0.753 1.99 2.183 0.8211958 0.067
slr1170 slr1170 -0.807 2.766 -0.676 0.308 0.668 -0.598 0.7363329 0.070
ycf38 sll0760 -0.445 2.748 -0.294 0.164 0.823 1.469 0.6739376 0.122
ssl0331 ssl0331 -1.177 2.738 1.195 1.389 0.463 -1.187 0.8598838 0.019
slr1693 slr1693 -1.723 2.719 0.576 0.898 -0.397 -1.131 0.8525814 0.045
psbO sll0427 0.64 2.707 0.222 0.633 3.339 1.65 0.8838887 0.078
psbD sll0849 -0.861 2.664 1.002 -0.005 3.231 3.101 0.8557861 0.192
slr1692 slr1692 0.437 2.655 0.33 0.811 1.705 1.623 0.8429081 0.039
hemB sll1994 -0.063 2.652 -0.914 0.697 0.646 0.836 0.6646820 0.110
slr2042 slr2042 -0.364 2.633 0.809 1.148 1.345 -1.302 0.8793763 0.008
cpcG slr2051 -0.205 2.621 0.183 0.472 2.294 1.485 0.9132484 0.027
slr1302 slr1302 -0.999 2.591 0.061 0.454 1.365 -0.191 0.8534901 0.026
moeB sll1536 -1.04 2.579 1.069 0.234 -0.366 -0.368 0.7776446 0.054
sll0148 sll0148 0.357 2.556 -0.37 -0.143 1.982 0.077 0.7719391 0.094
plsX slr1510 0.976 2.549 0.169 -0.006 0.186 1.868 0.6005493 0.205
sll6109 sll6109 -0.938 2.516 0.586 0.717 0.079 0.159 0.8704727 0.013
cysH slr1791 -1.968 2.512 0.674 1.242 0.068 -0.998 0.8428947 0.057
leuD sll1444 -2.209 2.505 -0.124 1.64 -1.451 -0.038 0.6714334 0.313
trxA3 slr0623 -1.336 2.493 1.078 1.881 0.16 -0.637 0.8097690 0.057
nrtB sll1451 -0.107 2.485 0.272 0.388 -0.244 1.229 0.8884861 0.012
slr1841 slr1841 -0.166 2.451 1.171 0.655 0.131 0.426 0.7466448 0.040
sll2003 sll2003 -0.423 2.396 0.519 0.236 1.625 0.597 0.8779608 0.022
drgA slr1719 -1.649 2.368 0.596 1.457 -0.317 -0.446 0.6695048 0.187
sll1380 sll1380 -0.367 2.364 0.551 0.741 1.707 0.674 0.9376518 0.006
petH slr1643 -1.23 2.351 1.048 1.592 2.039 -2.97 0.7531722 0.109
sll1500 sll1500 -1.256 2.346 -0.004 0.248 -0.861 -0.634 0.7141056 0.150
sll1304 sll1304 -0.655 2.334 0.667 0.558 0.589 0.622 0.8743538 0.010
sll0301 sll0301 0.045 2.33 -0.396 -0.094 2.231 0.605 0.9108020 0.034
slr0950 slr0950 -0.06 2.276 0.509 0.314 1.55 0.089 0.8557459 0.020
hemC slr1887 -0.702 2.265 0.206 0.998 0.118 0.461 0.6255683 0.111
trpF sll0356 0.03 2.257 0.118 0.505 -0.803 1.243 0.7586823 0.082
ndhF slr2009 0.221 2.237 0.905 0.613 2.258 3.08 0.9355070 0.043
slr1042 slr1042 0.571 2.208 0.633 0.498 0.785 0.821 0.5506013 0.163
proC slr0661 -0.746 2.205 0.115 -0.077 1.399 1.48 0.8763769 0.057
rpiA slr0194 -0.278 2.194 1.674 1.505 2.476 -1.452 0.9605876 0.003
thrA sll0455 0.548 2.169 -0.427 -0.459 0.803 0.359 0.6473974 0.170
glnA slr1756 -0.699 2.114 0.035 0.284 -1.379 0.004 0.8295902 0.089
slr0519 slr0519 -1.506 2.09 1.043 1.988 0.287 -1.1 0.6946988 0.190
ssl1918 ssl1918 -0.063 2.086 0.326 0.449 1.257 0.418 0.7483282 0.054
sll0847 sll0847 -0.875 2.075 -0.013 0.095 0.622 0.374 0.7363211 0.083
ccmK4 slr1839 -0.233 2.073 0.584 0.413 2.248 0.326 0.7885394 0.106
pdhB sll1721 -0.862 2.072 1.323 1.246 0.616 -0.273 0.7513861 0.044
sll1025 sll1025 0.419 2.061 0.377 0.473 1.319 0.495 0.7599508 0.059
prc slr1751 0.376 2.057 1.334 0.611 0.429 0.649 0.5909157 0.136
slr0771 slr0771 -0.084 2.042 0.311 0.342 0.239 -0.095 0.7574635 0.037
tufA sll1099 -1.461 2.041 1.399 2.218 0.036 -1.078 0.7057584 0.217
fabF sll1069 0.361 2.04 1.575 0.069 0.345 -0.025 0.8170498 0.048
aroB slr2130 -1.206 2.02 -0.656 0.772 -0.523 -0.045 0.8102623 0.094
nrtA sll1450 -0.214 2.019 0.246 0.238 -0.409 1.571 0.8602072 0.026
hhoB sll1427 -0.04 2.017 -0.198 0.237 2.207 0.158 0.7804217 0.129
slr0813 slr0813 1.839 -2.009 0.304 0.354 -0.981 0.595 0.7628332 0.187
slr1783 slr1783 -0.973 -2.014 -0.805 -0.173 -0.65 0.265 0.8726587 0.038
sll0176 sll0176 -0.408 -2.085 -2.021 -1.717 -1.526 0.952 0.6551709 0.151
slr1095 slr1095 2.429 -2.1 -1.284 -1.592 -0.274 1.777 0.6370579 0.325
clpP4 sll0534 -0.193 -2.123 -1.24 -0.652 -0.922 -0.194 0.7367912 0.042
sll5063 sll5063 -1.163 -2.128 -0.35 0.218 -0.272 0.018 0.8440277 0.070
mraY sll0657 1.211 -2.188 -0.758 -0.99 -0.843 0.787 0.8718734 0.017
sll0162 sll0162 0.229 -2.203 -0.403 -0.234 -0.405 0.062 0.6347022 0.078
fabF2 slr1332 0.401 -2.251 0.251 0.187 -2.097 0.38 0.9209766 0.021
slr1098 slr1098 0.903 -2.261 -0.199 -0.434 0.479 -2.543 0.4195152 0.403
sll1757 sll1757 0.783 -2.271 -0.494 -0.752 -1.562 -0.125 0.5383796 0.200
ftsZ sll1633 2.34 -2.314 -1.677 -1.439 0.318 2.136 0.9249829 0.039
gcvP slr0293 1.861 -2.335 -1.541 -0.695 -0.558 1.696 0.9451148 0.007
slr0484 slr0484 0.087 -2.342 -0.79 0.038 -0.367 -0.061 0.7395823 0.049
ssr0657 ssr0657 0.935 -2.358 -0.659 -0.992 -2.021 1.496 0.9170055 0.009
sll1915 sll1915 0.381 -2.385 -0.147 -0.431 -1.437 -0.118 0.6397709 0.110
dnaJ4 sll0897 0.955 -2.471 -0.087 0.165 -0.67 1.728 0.9304110 0.007
psaK ssr0390 1.17 -2.479 -1.404 -0.784 -0.729 0.717 0.6971797 0.062
ppiB sll0227 0.735 -2.528 -1.035 -0.173 -0.759 0.306 0.8520480 0.013
slr0643 slr0643 0.127 -2.545 -0.449 0.098 -0.647 0.279 0.5529642 0.140
sll0498 sll0498 0.407 -2.577 -1.166 -1.108 -2.148 -1.967 0.8482665 0.063
mrdB slr1267 0.179 -2.655 -0.252 -0.24 -1.365 0.704 0.8603778 0.014
rpoE slr1545 0.874 -2.759 -0.425 0.6 -1.246 1.277 0.5615657 0.194
sll0364 sll0364 2.834 -2.812 -0.108 1.758 0.601 3.578 0.8717770 0.119
clpX sll0535 0.736 -2.966 -1.054 -1.121 -1.503 0.01 0.7464408 0.035
sll0481 sll0481 2.141 -3.371 -1.601 -1.335 0.485 1.056 0.9686290 0.002
sll0877 sll0877 1.76 -3.694 -2 -1.39 -1.273 0.847 0.7839636 0.032
ycf19 ssr2142 1.071 -3.914 -0.278 0.284 -2.735 1.742 0.5878787 0.184
df_linreg_wide %>% color_table("-N")
sgRNA_target locus carbon light -N +FL +G +D r_squared pval_-N
atpA sll1326 -0.989 -0.069 2.047 0.064 0.272 -1.903 0.3425321 0.396
sll1878 sll1878 -1.585 4.291 2.016 2.165 1.301 -0.213 0.7988744 0.179
sll0176 sll0176 -0.408 -2.085 -2.021 -1.717 -1.526 0.952 0.6551709 0.168
slr1079 slr1079 1.742 -1.553 -2.022 -1.193 -0.394 1.843 0.5565884 0.304
sll5046 sll5046 1.34 -1.273 -2.118 -0.985 -0.329 1.365 0.6360797 0.152
ssr3532 ssr3532 2.303 -1.661 -3.733 -1.464 0.702 1.64 0.6071408 0.201
df_linreg_wide %>% color_table("+FL")
sgRNA_target locus carbon light -N +FL +G +D r_squared pval_+FL
rps15 ssl1784 -2.769 1.647 1.312 2.437 -0.758 -1.499 0.7223751 0.204
pyrG sll1443 -1.738 0.567 -0.111 2.307 -1.107 -0.398 0.6651024 0.212
sll0217 sll0217 -2.418 1.723 0.954 2.228 -0.286 -1.152 0.6281035 0.258
tufA sll1099 -1.461 2.041 1.399 2.218 0.036 -1.078 0.7057584 0.112
sll1878 sll1878 -1.585 4.291 2.016 2.165 1.301 -0.213 0.7988744 0.085
sll0933 sll0933 -2.634 1.271 0.081 2.115 -0.633 -0.702 0.6558952 0.294
fus2 sll1098 -1.02 1.482 0.038 2.077 0.13 -0.815 0.7239763 0.087
slr0007 slr0007 -2.308 1.932 0.695 2.012 -0.851 -1.068 0.6792795 0.267
ribC sll0300 -1.073 -0.365 -0.267 -2.057 -0.721 -0.724 0.7973653 0.037
entC slr0817 -0.511 -0.844 -0.456 -2.123 2.558 0.74 0.9773035 0.009
ribA sll1894 -0.927 -0.39 -0.139 -2.37 -0.725 -0.64 0.6200967 0.101
sll1521 sll1521 -0.353 -0.754 -0.52 -3.993 -0.671 -0.342 0.9636366 0.001
df_linreg_wide %>% color_table("+G")
sgRNA_target locus carbon light -N +FL +G +D r_squared pval_+G
apcE slr0335 -0.681 5.071 0.314 1.093 3.962 1.65 0.8255851 0.056
dgt sll0398 -0.463 0.606 0.269 1.052 3.782 -0.261 0.9841219 0.000
psbO sll0427 0.64 2.707 0.222 0.633 3.339 1.65 0.8838887 0.021
apcA slr2067 0.027 4.441 0.576 0.809 3.301 2.922 0.7380667 0.142
psbC sll0851 -0.001 1.691 0.24 0.281 3.288 3.75 0.9364716 0.019
sll1496 sll1496 1.499 -1.248 -0.507 -0.657 3.237 -2.22 0.9150619 0.013
psbD sll0849 -0.861 2.664 1.002 -0.005 3.231 3.101 0.8557861 0.072
psbJ smr0008 0.192 2.968 0.293 0.419 3.216 3.222 0.8923081 0.037
slr0758 slr0758 -0.644 1.944 0.251 -0.176 2.791 -1.499 0.9317621 0.005
sll1378 sll1378 -0.955 3.701 -0.4 1.2 2.782 0.937 0.9545533 0.006
slr1102 slr1102 0.056 3.162 -0.118 0.511 2.737 0.819 0.8207709 0.050
sll0556 sll0556 1.268 0.982 0.25 -0.377 2.727 0.121 0.7859377 0.060
slr2070 slr2070 0.846 -0.597 -0.408 -0.608 2.711 -1.752 0.7416662 0.075
cbbA sll0018 -0.644 1.164 0.415 0.545 2.607 2.054 0.9537621 0.008
entC slr0817 -0.511 -0.844 -0.456 -2.123 2.558 0.74 0.9773035 0.005
cpcB sll1577 0.12 4.032 0.141 0.74 2.544 2.476 0.8276584 0.093
ssr2062 ssr2062 0.5 0.071 0.167 -0.086 2.528 -1.616 0.9322018 0.004
rpiA slr0194 -0.278 2.194 1.674 1.505 2.476 -1.452 0.9605876 0.001
slr1990 slr1990 -0.768 3.219 0.197 0.994 2.472 2.429 0.8920252 0.047
trx sll1057 -0.491 1.161 -0.106 0.132 2.417 0.6 0.9611652 0.003
sll6055 sll6055 -1.122 3.123 0.558 0.514 2.406 2.128 0.8376624 0.094
psbH ssl2598 0.265 1.696 0.215 -0.134 2.349 1.235 0.8920776 0.024
cyp2 slr0574 -0.6 3.073 0.867 1.027 2.347 -0.438 0.9444943 0.003
ccsA sll1513 0.287 1.628 0.096 0.428 2.321 0.814 0.9010260 0.013
cpcC2 sll1579 -0.036 1.144 0.101 0.224 2.32 0.641 0.9783295 0.001
cpcG slr2051 -0.205 2.621 0.183 0.472 2.294 1.485 0.9132484 0.019
sll0062 sll0062 0.605 1.181 0.165 0.639 2.262 1.004 0.8522771 0.027
ndhF slr2009 0.221 2.237 0.905 0.613 2.258 3.08 0.9355070 0.020
cpcA sll1578 0.11 3.57 0.261 0.609 2.255 2.162 0.8434275 0.079
ccmK4 slr1839 -0.233 2.073 0.584 0.413 2.248 0.326 0.7885394 0.045
apcB slr1986 0.627 2.876 0.319 0.432 2.241 2.188 0.7619664 0.130
sll0301 sll0301 0.045 2.33 -0.396 -0.094 2.231 0.605 0.9108020 0.018
slr1505 slr1505 -0.682 2.926 0.456 0.864 2.222 3.068 0.9263147 0.035
hhoB sll1427 -0.04 2.017 -0.198 0.237 2.207 0.158 0.7804217 0.056
rub slr2033 0.214 1.429 0.237 -0.416 2.109 1.991 0.9937546 0.000
petH slr1643 -1.23 2.351 1.048 1.592 2.039 -2.97 0.7531722 0.085
ssr0657 ssr0657 0.935 -2.358 -0.659 -0.992 -2.021 1.496 0.9170055 0.007
fabF2 slr1332 0.401 -2.251 0.251 0.187 -2.097 0.38 0.9209766 0.012
sll0498 sll0498 0.407 -2.577 -1.166 -1.108 -2.148 -1.967 0.8482665 0.053
slr1974 slr1974 -1.295 -0.202 0.509 0.64 -2.153 -0.861 0.7460569 0.127
glcP sll0771 0.113 -0.381 -0.143 0.008 -2.164 -1.027 0.9982143 0.000
minE ssl0546 0.975 -1.768 -1.032 -0.849 -2.194 0.18 0.8244200 0.032
purA sll1823 -1.4 -0.755 1.149 0.416 -2.4 -0.727 0.6987707 0.151
glgA sll1393 -0.137 -0.226 0.031 -0.204 -2.7 1.505 0.9275690 0.004
pilT sll1533 -1.207 -0.456 -0.979 1.343 -2.71 0.646 0.7070054 0.138
ycf19 ssr2142 1.071 -3.914 -0.278 0.284 -2.735 1.742 0.5878787 0.226
ssl3364 ssl3364 0.214 -1.289 -0.623 -0.271 -3.033 -0.61 0.9116291 0.010
glk sll0593 -0.008 -0.723 -0.614 -0.394 -3.701 -2.08 0.9860610 0.000
df_linreg_wide %>% color_table("+D")
sgRNA_target locus carbon light -N +FL +G +D r_squared pval_+D
psbB slr0906 -0.064 1.707 0.622 -0.176 1.44 4.62 0.8709903 0.028
psbE ssr3451 0.191 1.559 0.07 0.163 1.443 3.846 0.9215596 0.012
psbC sll0851 -0.001 1.691 0.24 0.281 3.288 3.75 0.9364716 0.030
psbF smr0006 0.201 1.328 0.045 -0.021 1.078 3.584 0.9289141 0.009
sll0364 sll0364 2.834 -2.812 -0.108 1.758 0.601 3.578 0.8717770 0.072
rpoDI slr0653 1.855 -0.679 -1.12 -1.39 -1.275 3.412 0.5935773 0.155
psbD2 slr0927 -0.005 1.286 0.228 -0.241 1.923 3.353 0.9093335 0.031
psbJ smr0008 0.192 2.968 0.293 0.419 3.216 3.222 0.8923081 0.079
psbD sll0849 -0.861 2.664 1.002 -0.005 3.231 3.101 0.8557861 0.152
ndhF slr2009 0.221 2.237 0.905 0.613 2.258 3.08 0.9355070 0.018
slr1505 slr1505 -0.682 2.926 0.456 0.864 2.222 3.068 0.9263147 0.031
tktA sll1070 0.198 0.59 0.503 0.822 1.981 2.992 0.9639206 0.006
apcA slr2067 0.027 4.441 0.576 0.809 3.301 2.922 0.7380667 0.289
lysA sll0504 -0.206 0.251 0.394 0.088 0.396 2.767 0.9715674 0.001
cpcB sll1577 0.12 4.032 0.141 0.74 2.544 2.476 0.8276584 0.181
slr1990 slr1990 -0.768 3.219 0.197 0.994 2.472 2.429 0.8920252 0.102
psbL smr0007 0.139 1.635 0.417 0.57 1.385 2.402 0.8022242 0.083
apcB slr1986 0.627 2.876 0.319 0.432 2.241 2.188 0.7619664 0.232
slr0734 slr0734 0.52 2.819 0.607 0.753 1.99 2.183 0.8211958 0.134
hemA slr1808 0.905 1.077 -0.68 -0.639 1.149 2.174 0.5761540 0.327
cpcA sll1578 0.11 3.57 0.261 0.609 2.255 2.162 0.8434275 0.164
ftsZ sll1633 2.34 -2.314 -1.677 -1.439 0.318 2.136 0.9249829 0.054
sll6055 sll6055 -1.122 3.123 0.558 0.514 2.406 2.128 0.8376624 0.217
sll0930 sll0930 0.084 0.116 0.329 0.368 1.209 2.055 0.8790537 0.046
cbbA sll0018 -0.644 1.164 0.415 0.545 2.607 2.054 0.9537621 0.043
sll0983 sll0983 1.392 -1.936 0.075 -0.306 -0.69 2.036 0.8763437 0.041
glk sll0593 -0.008 -0.723 -0.614 -0.394 -3.701 -2.08 0.9860610 0.012
ndhC slr1279 0.96 -0.173 0.393 -0.18 -0.985 -2.147 0.8635036 0.098
ycf43 sll0194 -0.722 -0.119 0.025 0.102 1.626 -2.206 0.9735251 0.001
sll1496 sll1496 1.499 -1.248 -0.507 -0.657 3.237 -2.22 0.9150619 0.088
pmgA sll1968 1.162 -1.905 0.387 -1.343 -1.769 -2.242 0.5849242 0.429
ssr2333 ssr2333 0.305 -1.552 -0.003 -0.034 0.305 -2.319 0.4255312 0.285
ssl0438 ssl0438 0.12 -1.474 -0.18 -0.419 -0.219 -2.375 0.8654067 0.019
atpI sll1322 -1.547 -0.422 1.294 0.662 0.252 -2.475 0.4121488 0.353
slr1098 slr1098 0.903 -2.261 -0.199 -0.434 0.479 -2.543 0.4195152 0.367
talB slr1793 0.152 -0.144 -0.806 -0.023 0.111 -2.879 0.9813772 0.000
petH slr1643 -1.23 2.351 1.048 1.592 2.039 -2.97 0.7531722 0.066
zwf slr1843 -0.079 0.297 -0.149 0.054 -0.043 -2.998 0.9966699 0.000
slr1245 slr1245 -2.694 1.826 1.245 -0.805 1.092 -3.224 0.8325233 0.067

Based on the multiple linear model correlations, we can try to extract a shortlist of the most interesting hypothetical genes. These could warrant further investigations.

list_top_unknown_hits <- df_linreg_wide %>%
  left_join(df_uniprot, by = "locus") %>%
  # filter by name: only unknown proteins
  filter(
    is.na(gene_name_short),
    str_detect(protein, "[a-zA-Z]{3}[0-9]{4} protein|Uncharacterized")) %>%
  # filter by effect: only correlation > 3
  filter(if_any(matches("^(carb|light|\\-|\\+)"), ~ abs(.) > 3)) %>%
  arrange(desc(r_squared)) %>%
  pull(locus)

df_linreg_wide %>% filter(locus %in% list_top_unknown_hits) %>%
  select(!starts_with("pval"), -sgRNA_target) %>%
  mutate(across(2:7, ~ cell_spec(., "html", color = "white",
      background = spec_color(., option = "E", scale = c(-5.5, 5.5)),
      bold = TRUE))) %>%
  kbl(format = "html", escape = F) %>%
  kable_paper("striped", full_width = F)
locus carbon light -N +FL +G +D r_squared
sll0364 2.834 -2.812 -0.108 1.758 0.601 3.578 0.8717770
sll0481 2.141 -3.371 -1.601 -1.335 0.485 1.056 0.9686290
sll0877 1.76 -3.694 -2 -1.39 -1.273 0.847 0.7839636
sll1378 -0.955 3.701 -0.4 1.2 2.782 0.937 0.9545533
sll6055 -1.122 3.123 0.558 0.514 2.406 2.128 0.8376624
slr1102 0.056 3.162 -0.118 0.511 2.737 0.819 0.8207709
slr1505 -0.682 2.926 0.456 0.864 2.222 3.068 0.9263147
slr1990 -0.768 3.219 0.197 0.994 2.472 2.429 0.8920252
ssl3364 0.214 -1.289 -0.623 -0.271 -3.033 -0.61 0.9116291
ssr3532 2.303 -1.661 -3.733 -1.464 0.702 1.64 0.6071408

8.5 Extract and analyze interesting gene clusters

The list above shows the genes whose fitness is most significantly correlated with one of the treatments. This list of genes is extracted and then simply fitness per condition is plotted as a heatmap, in order to confirm the trends from fitting the multiple liner regression models.

plot_sgRNAs_light <- df_gene %>%
  filter(locus %in% list_top_unknown_hits, time == 0) %>%
  mutate(sgRNA_target = fct_cluster(sgRNA_target, condition, wmean_fitness)) %>%
  mutate(condition = fct_cluster(condition, sgRNA_target, wmean_fitness)) %>%
  mutate(wmean_fitness = wmean_fitness %>% replace(., . > 4, 4) %>% replace(., . < -4, -4)) %>%
  ggplot(aes(x = condition, y = sgRNA_target, fill = wmean_fitness)) +
  geom_tile() + custom_theme() +
  labs(title = "Top unknown genes", x = "", y = "") +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1)) +
  scale_fill_gradientn(colours = c(custom_colors[1], grey(0.9), custom_colors[2]),
    limits = c(-4, 4))

print(plot_sgRNAs_light)

save_plot(plot_sgRNAs_light, width = 8.0, height = 3.5)

Summary

  • sll0364 - 139 AA. KD has higher fitness in HC conditions and lower fitness in HL. Negatively regulating carbon metabolism?
  • sll0481 - 155 AA. KD has higher fitness in +G conditions and lower fitness in HL. Membrane localization. Negatively regulating glycolysis?
  • sll0877 - 456 AA. KD has higher fitness only in HC,LL. Mitigates light limitation?
  • ssl3364 - 74 AA. KD has lower fitness on all HC/+G conditions. This protein is known as CP12 protein, regulating glycolytic flux at GAPDH and PRK.
  • ssr3532 - 80 AA. KD lower fitness on N-limitation and C-limitation (LC-HL combinations). Same operon as glutaminase glsA (slr2079, catalyzes deamination of gln –> glu), regulatory, involved in N metabolism?
  • slr1990 - 240 AA, 5 TM domains. KD higher fitness in photoheterotrophy, lower fitness in all HC/LL conditions. Something important for photosystems? Something that wastes e- in photoheterotrophic conditions?
  • sll6055 - 152 AA. Fitness profile as above. Multiubiquitin domain, involved in protein modification/degradation of PS proteins?
  • slr1505 - 198 AA. Fitness profile as above. No useful information.
  • sll1378 - 300 AA. KD has lower fitness on all LL conditions. Membrane associated protein? In STRING, potential interaction with PbsA1 and PbsA2 (Heme oxygenase 1 and 2). Potentially important for chlorophyll or heme biosynthesis –> would explain importance for photosynthesis in LL condition.
  • slr1102 - 853 AA. KD has lower fitness on all LL conditions. 4 known domains, FHA (forkhead-associated domain is a phosphopeptide recognition domain found in many regulatory proteins), PAS (signaling, often involved in circadian proteins, detect their signal by way of an associated cofactor like heme, flavin), GGDEF (involved in signal transduction, likely to catalyze synthesis or hydrolysis of cyclic diguanylate c-diGMP), EAL (shown to stimulate degradation of a second messenger, cyclic di-GMP, candidate for a diguanylate phosphodiesterase function. Together with the GGDEF domain, EAL might be involved in regulating cell surface adhesiveness in bacteria). Source: InterPro. Embedded in a tight network of interacting proteins all involved in chromophore biosynthesis/maturation.

Apc and cpc repression mutants encoding phycobilisomes are also enriched in high light

plot_sgRNAs_phycobil <- df_gene %>%
  filter(str_detect(gene_name, "[ac]pc"), time == 0) %>%
  mutate(wmean_fitness = wmean_fitness %>% replace(., . > 4, 4) %>% replace(., . < -4, -4)) %>%
  ggplot(aes(x = condition, y = fct_rev(sgRNA_target), fill = wmean_fitness)) +
  geom_tile() + custom_theme() +
  labs(title = "Apc/Cpc repression mutants", x = "", y = "") +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1)) +
  scale_fill_gradientn(colours = c(custom_colors[1], grey(0.9), custom_colors[2]),
    limits = c(-4, 4))

print(plot_sgRNAs_phycobil)

save_plot(plot_sgRNAs_phycobil, width = 6.5, height = 3.5)

9 Direct comparison of gene fitness

9.1 Fitness of all conditions vs each other

We can plot selected conditions against each other and add gene labels in order to find or confirm particular patterns.

make_fitness_plot <- function(data, vars, title = NULL) {
  # prepare data for two  variables each
  data %>% ungroup %>%
    filter(condition %in% vars, sgRNA_type == "gene") %>%
    select(locus, sgRNA_target, condition, wmean_fitness) %>% distinct %>%
    pivot_wider(names_from = condition, values_from = wmean_fitness) %>%
    mutate(
      dfit = get(vars[1]) - get(vars[2]),
      significant = !between(dfit, quantile(dfit, probs = c(0.003)),
        quantile(dfit, probs = c(0.997))),
      sgRNA_target = if_else(significant, sgRNA_target, "")) %>%
    
    # plot
    ggplot(aes(x = get(vars[1]), y = get(vars[2]), 
      color = significant, label = sgRNA_target)) +
    geom_point(size = 1) + custom_theme(legend.position = 0) +
    geom_abline(intercept = 0, slope = 1, col = grey(0.5), lty = 2, size = 0.8) +
    geom_abline(intercept = 4, slope = 1, col = grey(0.5), lty = 2, size = 0.8) +
    geom_abline(intercept = -4, slope = 1, col = grey(0.5), lty = 2, size = 0.8) +
    geom_text_repel(size = 3, max.overlaps = 50) +
    labs(title = title, x = vars[1], y = vars[2]) +
    coord_cartesian(xlim = c(-9, 5), ylim = c(-9, 5)) +
    scale_color_manual(values = c(grey(0.5), custom_colors[2]))
}

# browse through all possible condition combinations;
# we need a helper function that detects duplicated combinations
duplicated_2vec <- function(x, y) {
  xy = paste(x, y); yx = paste(y, x)
  sapply(xy, function(xval) {
    which(xval == yx) <= which(xval == xy)
  })
}

list_condition_pairs <- lapply(
  unique(df_gene$condition) %>% expand_grid(x = ., y = .) %>%
    filter(!duplicated_2vec(x, y)) %>% t %>% as.data.frame %>% as.list,
  function(var) {
    make_fitness_plot(df_gene, vars = var,
      title = paste(var, collapse = "  -  "))
  }
)

# export images
invisible(capture.output(
  lapply(list_condition_pairs, function(pl) {
    pl_name <- paste0("../figures/pairwise_comparisons/plot_", pl$labels$x, "_", pl$labels$y, ".png")
    png(filename = pl_name, width = 800, height = 800, res = 120)
    print(pl)
    dev.off()
  })
))
# example of first 4 combinations
list_condition_pairs[1:4]
$V1

$V2

$V3

$V4

10 Differential fitness of selected gene sets

10.1 Central carbon metabolism

To plot gene fitness for the enzymes of central carbon metabolism, we use the complete list of enzymes and the genes that they are mapped to (obtained from KEGG). We can extract gene sets for specific pathways and plot fitness. We start with glycolysis and Calvin cycle enzymes.

list_central_met_pathways <- c(
  "Glycolysis / Gluconeogenesis",
  "Pentose phosphate pathway",
  "Carbon fixation in photosynthetic organisms",
  "Photosynthesis",
  "Citrate cycle (TCA cycle)",
  "Pyruvate metabolism",
  "Glyoxylate and dicarboxylate metabolism"
)
plot_gene_fitness <- function(df, pw = NULL, gene = NULL,
  title = NULL, ncol = 8, legend.position = "bottom") {
  df <- df %>% filter(time == 0)
  if (!is.null(pw)) {
    df <- df %>% inner_join(df_kegg %>% filter(kegg_pathway == pw) %>% select(locus),
      by = "locus")
    title <- pw
  } else if (!is.null(gene)) {
    df <- df %>% filter(locus %in% gene)
  }
  
  ggplot(df, aes(x = condition, y = wmean_fitness, 
    ymin = wmean_fitness-sd_fitness, 
    ymax = wmean_fitness+sd_fitness, fill = condition, color = condition)) +
    geom_col(position = "dodge", width = 0.6) +
    geom_errorbar(position = "dodge", width = 0.6, size = 1) +
    custom_theme(aspect.ratio = 1,
      legend.position = legend.position, legend.key.size = unit(0.4, "cm")) + 
    labs(title = title, x = "", y = "fitness") +
    theme(axis.text.x = element_blank(), axis.ticks = element_blank()) +
    scale_fill_manual(values = colorRampPalette(custom_colors[1:5])(11)) +
    scale_color_manual(values = colorRampPalette(custom_colors[1:5])(11)) +
    facet_wrap(~ sgRNA_target, ncol = ncol, drop = FALSE)
}
print(plot_gene_fitness(df_gene, pw = list_central_met_pathways[[1]]))

ggsave("../figures/plot_fitness_glycolysis.svg",
  plot_gene_fitness(df_gene, pw = list_central_met_pathways[[1]]),
  width = 8, height = 6)
print(plot_gene_fitness(df_gene, pw = list_central_met_pathways[[2]]))

ggsave("../figures/plot_fitness_pentose.svg",
  plot_gene_fitness(df_gene, pw = list_central_met_pathways[[2]]),
  width = 8, height = 5)
print(plot_gene_fitness(df_gene, pw = list_central_met_pathways[[3]]))

ggsave("../figures/plot_fitness_carbonfix.svg",
  plot_gene_fitness(df_gene, pw = list_central_met_pathways[[3]]),
  width = 8, height = 5)
print(plot_gene_fitness(df_gene, pw = list_central_met_pathways[[5]]))

ggsave("../figures/plot_fitness_citrate.svg",
  plot_gene_fitness(df_gene, pw = list_central_met_pathways[[5]]),
  width = 8, height = 4)

10.2 Gene fitness in mixotrophy and heterotrophy

Using fluctuator, we can import a custom metabolic map for Synechocystis sp. PCC 6803, and overlay published fluxes that were measured with LC-MS using isotopically labelled carbon sources (Nakajima et al., 2014).

Fluctuator can be installed using a function from devtools:

devtools::install_github("m-jahn/fluctuator")

We import the metabolic flux data from the supplemental items of Nakajima et al., 2014.

library(fluctuator)

# import flux data
df_nakajima_mfa <- read.csv("../data/input/Nakajima2014_metabolic_fluxes.csv")

# generate stroke width and color
df_nakajima_mfa <- df_nakajima_mfa %>%
  mutate(
    stroke_width = 0.3 + (0.7*sqrt(abs(flux))),
    stroke_color = abs(flux) %>% {1+(./max(.))*9} %>% round,
    stroke_color_rgb =  colorRampPalette(custom_colors[c(5,2,1)])(10)[stroke_color])

The next step is to overlay the fluxes. We generate two types of maps, mixotrophy and photoheterotrophy. The stroke width and color for all reactions is set by the flux magnitude.

for (cond in c("mixotroph", "photoheterotroph")) {
  # import map 
  SVG_template <- read_svg("../data/input/map_central_metabolism_syn.svg")
  
  # set stroke on SVG map
  SVG_mix <- set_attributes(SVG_template,
    node = filter(df_nakajima_mfa, condition == cond)$reaction,
    attr = "style",
    pattern = "stroke-width:[0-9]+\\.[0-9]+",
    replacement = paste0("stroke-width:",
      filter(df_nakajima_mfa, condition == cond)$stroke_width))
  
  # set color
  SVG_mix <- set_attributes(SVG_mix,
    node = filter(df_nakajima_mfa, condition == cond)$reaction,
    attr = "style",
    pattern = "stroke:#b3b3b3",
    replacement = paste0("stroke:",
      filter(df_nakajima_mfa, condition == cond)$stroke_color_rgb))
  
  # set arrow directionality
  SVG_mix <- set_attributes(SVG_mix,
    node = filter(df_nakajima_mfa, condition == cond, flux < 0)$reaction,
    attr = "style",
    pattern = "marker-end:url\\(#marker[0-9]*\\);",
    replacement = "")
  
  SVG_mix <- set_attributes(SVG_mix,
    node = filter(df_nakajima_mfa, condition == cond, flux > 0)$reaction,
    attr = "style",
    pattern = "marker-start:url\\(#marker[0-9]*\\);",
    replacement = "")
  
  write_svg(SVG_mix, file = paste0("../data/output/map_", cond, "y.svg"))
}
Metabolic flux with mixotrophy Metabolic flux with photoheterotrophy

Now we plot fitness of central carbon metabolism genes for two or three selected conditions. These will be added to the metabolic map manually. The mixotrophic conditions LC, LL, +G and HC, LL, +G turned out to be very similar.

df_centralcarb <- tibble(
  locus = c(   "sll0593", "slr0329", "slr1843", "sll1479", "sll0329", "slr1349",
    "slr0952", "slr2094", "slr0943", "sll0018", "slr0783", "sll1342", "slr0884",
    "slr0394", "slr1945", "slr0752", "sll0587", "sll1275", "sll1070", "slr1793",
    "slr0194", "ssl2153", "sll0807", "sll1525", "slr0009", "slr0012", "sll1721",
    "sll1841", "slr1096", "slr1934", "sll0401", "slr0665", "slr1289", "slr1096",
    "sll1023", "sll1557", "sll0823", "sll1625", "slr0201", "slr1233", "slr0018",
    "sll0891", "sll0920", "slr0721"),
  reaction = c("HEX", "HEX", "G6PDH", "PGL", "GND", "PGI", "FBP",
    "FBP", "FBA", "FBA", "TPI", "GAPDH", "GAPDH", "PGK", "PGM", "ENO",
    "PYK", "PYK", "TKT", "TAL", "RPI", "RPI", "RPE", "PRUK", "RUBISCO",
    "RUBISCO", "PDH", "PDH", "PDH", "PDH", "CS", "ACONT", "ICDH", "AKGDH",
    "SUCOAS", "SUCOAS", "SUCD", "SUCD", "SUCD", "SUCD", "FUM", "MDH",
    "PPC", "ME")) %>%
  inner_join(df_kegg) %>% group_by(locus) %>% slice(1) %>%
  ungroup %>% arrange(reaction)
Joining, by = "locus"
plot_centralcarb_minifig <- df_gene %>% filter(
    time == 0,
    condition %in% c("LC, LL", "LC, LL, +G", "LC, LL, +D, +G")) %>%
  inner_join(df_centralcarb, by = "locus") %>%
  mutate(sgRNA_target = paste0(reaction, " (", sgRNA_target, ")")) %>%
  mutate(condition = factor(condition, c("LC, LL", "LC, LL, +G", "LC, LL, +D, +G"))) %>%
  
  ggplot(aes(x = condition, y = wmean_fitness, 
    ymin = wmean_fitness-sd_fitness, 
    ymax = wmean_fitness+sd_fitness, fill = condition, color = condition)) +
  geom_hline(yintercept = c(0, -5, -10), linetype = 3, col = grey(0.6)) +
  geom_col(position = "dodge", width = 0.6) +
  geom_errorbar(position = "dodge", width = 0.6, size = 1) +
  custom_theme(aspect.ratio = 1,
    legend.position = "bottom", legend.key.size = unit(0.4, "cm")) + 
  theme(axis.text.x = element_blank(), axis.text.y = element_blank(), 
    axis.ticks = element_blank(), panel.grid.major = element_blank(),
    strip.text = element_text(size = 8)) +
  labs(x = "", y = "") +
  coord_cartesian(ylim = c(-11, 1)) +
  scale_fill_manual(values = custom_colors[c(5,2,3)]) +
  scale_color_manual(values = custom_colors[c(5,2,3)]) +
  facet_wrap(~ sgRNA_target, ncol = 9, drop = FALSE)

plot_centralcarb_minifig

Similar but more concisely, we can test if there is a correaltion between flux and fitness penalty upon repression. Theoretically, such a correlation should exist because repression of high flux enzymes should have the strongest penalty on fitness. However, examination of the data does not reveal a clear correlation. Causes for this may be manifold, including the compensation of gene duplicates/iso-enzymes for gene knock down at high flux reactions.

df_gene %>% filter(
    time == 0,
    condition %in% c("LC, LL, +G", "LC, LL, +D, +G")) %>%
  mutate(condition = recode(condition, 
    `LC, LL, +G` = "mixotroph", `LC, LL, +D, +G` = "photoheterotroph")) %>%
  inner_join(df_centralcarb, by = "locus") %>%
  select(sgRNA_target, locus, gene_name, condition, wmean_fitness, sd_fitness, reaction) %>%
  inner_join(by = c("condition", "reaction"),
    select(df_nakajima_mfa, condition, reaction, flux, ci_low, ci_high)) %>%
  
  ggplot(aes(x = abs(flux), y = abs(wmean_fitness),
    ymin = abs(wmean_fitness)-sd_fitness, 
    ymax = abs(wmean_fitness)+sd_fitness,
    xmin = abs(ci_low), 
    xmax = abs(ci_high))) +
  geom_errorbar(orientation = "x", col = grey(0.75)) +
  geom_errorbar(orientation = "y", col = grey(0.75)) +
  geom_point() +
  coord_cartesian(xlim = c(-0.2, 3.2), ylim = c(-0.5, 7.5)) +
  custom_theme(aspect.ratio = 1) + 
  facet_wrap(~ condition, ncol = 2)

10.3 Adaptation to light and carbon excess

We will look at three different types of regulatory adaptations:

  • apc/cpcantenna proteins (phycobilisomes), known to be among the most expressed and regulated genes in cyanos
  • flavoproteins Flv1 (sll1521), Flv2 (sll0219), Flv3 (sll0550), Flv4 (sll0217), sll0218 (in flv2/4 operon)
  • low affinity/high flux transporters Ci transporters: bicA (sll0834), NDH-I4 with ndhF4, D4, cupB (sll0026, sll0027, slr1302)
  • high affinity/low flux inducible Ci transporters: BCT1/cmpAB(porB)CD (slr0040-44), SbtA/B (slr1512, slr1513), NDH-I3 with ndhF3, ndhD3, cupA, cupS (sll1732-35)
  • carbon transport regulatory proteins: ccmR/rbcR (sll1594), cmpR (sll0030), cyabrB1 (sll0359), cyabrB2 (sll0822)
plot_phycobilisome <- df_gene %>% filter(str_detect(gene_name, "[ac]pc[ABCDEFG]")) %>%
  plot_gene_fitness(ncol = 6, legend.position = 0)

plot_flv_genes <- df_gene %>% filter(locus %in% c("sll1521", "sll0219", "sll0550", "sll0217", "sll0218")) %>%
  mutate(sgRNA_target = recode(sgRNA_target, `sll1521` = "Flv1 (sll1521)", `sll0219` = "Flv2 (sll0219)",
    `sll0550` = "Flv3 (sll0550)", `sll0217` = "Flv4 (sll0217)")) %>%
  mutate(sgRNA_target = factor(sgRNA_target, c(unique(sgRNA_target), ""))) %>%
  plot_gene_fitness(ncol = 6, legend.position = 0)

plot_carbon_uptake <- df_gene %>% filter(locus %in% c(
    "sll0026", "sll0027", "slr1302",
    "sll1732", "sll1733", "sll1734", "sll1735", "slr0040", "slr0041","slr0043","slr0044"
  )) %>%
  mutate(sgRNA_target = recode(sgRNA_target,
    `nrtC2` = "cmpC", `nrtD3` = "cmpD",
    `sll1734` = "cupA", `slr1302` = "cupB",
    `sll1735` = "cupS", `ndhF2` = "ndhF3"
  )) %>%
  mutate(sgRNA_target = factor(sgRNA_target, unique(sgRNA_target)[c(4,6,11,3,5,9,10,1,2,7,8)])) %>%
  plot_gene_fitness(ncol = 6, legend.position = 0) +
  coord_cartesian(ylim = c(-7.9, 2.4))

Figure 3 draft:

ggarrange(nrow = 3, heights =  c(0.47, 0.2, 0.33), labels = LETTERS[1:3], font.label = list_fontpars,
  plot_phycobilisome,
  plot_flv_genes,
  plot_carbon_uptake
)

As a Supplementary figure to C), we can plot all other carbon transporters and regulatory genes that showed a less remarkable effect.

plot_carbon_uptake_2 <- df_gene %>% filter(locus %in% c(
    "sll0834", "slr1512", "slr1513", "sll1594", "sll0030", "sll0359", "sll0822"
  )) %>%
  mutate(sgRNA_target = recode(sgRNA_target,
    `sll0834` = "bicA", `slr1512` = "sbtA", `slr1513` = "sbtB",
    `sll0359` = "cyabrB1", `sll0822` = "cyabrB2", `rbcR` = "ccmR"
  )) %>%
  mutate(sgRNA_target = factor(sgRNA_target, unique(sgRNA_target))) %>%
  plot_gene_fitness(ncol = 4, legend.position = "right")

plot_carbon_uptake_2

As another Supplementary Figure, we can plot the total protein mass of the phycobilisome determined by protein mass spectrometry. This data was published in our study Jahn et al., Cell Reports, 2018. The data can be downloaded directly from the ShinyProt github page where it is included for on demand visualization.

load(url("https://github.com/m-jahn/ShinyProt/blob/master/data/Jahn_2018_Light_and_CO2_lim.Rdata?raw=true"))

plot_protmass_phycobilisome1 <- Jahn_2018_Light_and_CO2_lim %>%
  filter(str_detect(protein, "[ac]pc[ABCDEFG]"), sample != "CO2") %>%
  mutate(protein = str_extract(protein, "[ac]pc[ABCDEFG][12]?")) %>%
  ggplot(aes(x = factor(light), y = 100*mean_mass_fraction_norm, 
  fill = str_sub(protein, 1, 3), label = protein)) +
  lims(y = c(0, 22)) +
  geom_col(position = "stack", width = 0.7, col = grey(1), size = 0.2) +
  geom_text(size = 2.5, position = position_stack(vjust = 0.5), color = "white") +
  custom_theme(legend.position = "bottom", legend.key.size = unit(0.5, "cm")) +
  labs(title = "Light limitation", x = "µmol photons m^-2 s^-1", y = "% protein mass") +
  scale_fill_manual(values = custom_colors[c(2,4)]) +
  scale_color_manual(values = custom_colors[c(2,4)])

plot_protmass_phycobilisome2 <- Jahn_2018_Light_and_CO2_lim %>%
  filter(str_detect(protein, "[ac]pc[ABCDEFG]"), sample == "CO2") %>%
  mutate(protein = str_extract(protein, "[ac]pc[ABCDEFG][12]?")) %>%
  ggplot(aes(x = factor(co2_concentration), y = 100*mean_mass_fraction_norm, 
  fill = str_sub(protein, 1, 3), label = protein)) +
  lims(y = c(0, 22)) +
  geom_col(position = "stack", width = 0.7, col = grey(1), size = 0.2) +
  geom_text(size = 2.5, position = position_stack(vjust = 0.5), color = "white") +
  custom_theme(legend.position = "bottom", legend.key.size = unit(0.5, "cm")) +
  labs(title = "CO2 limitation", x = "% CO2 in air", y = "% protein mass") +
  scale_fill_manual(values = custom_colors[c(2,4)]) +
  scale_color_manual(values = custom_colors[c(2,4)])

ggarrange(ncol = 2, widths = c(0.5,0.5),
  labels = LETTERS[1:2], font.label = list_fontpars,
  plot_protmass_phycobilisome1,
  plot_protmass_phycobilisome2
)

Other genes of interest that either did not show any (remarkable) effect on fitness, or do not meet the scope of this section:

  • OCP (slr1963), pgr5 (ssr2016)
  • SigB (sll0306), SigC (sll0184), SigD (sll2012), SigE (sll1689) (rpoD genes 1-4)
  • ccmM (sll1031), ccmK2 (sll1028), ccmK1 (sll1029), ccmN (sll1032), ccmO (slr0436), ccmL (sll1030)
  • CP12 (ssl3364)

10.4 Genes where knock down leads to increased fitness

list_genes_pos_fitness <- df_gene %>%
  filter(time == 0, !is.na(locus), wmean_fitness > 2) %>%
  pull(locus) %>% unique

plot_gene_fitness(df_gene, gene = list_genes_pos_fitness, title = "Genes with increased fitness (f > 2)")

ggsave("../figures/plot_fitness_increased.svg",
  plot_gene_fitness(df_gene, gene = list_genes_pos_fitness, title = "Genes with increased fitness (f > 2)"),
  width = 8, height = 8)

Summary: - pmgA is once again the gene with strongest and most widespread fitness increase, validating results from library V1 - slr1916 same phenotype as pmgA just weaker. We also know this one from before. Must have identical role as pmgA. - all PSII genes show increased fitness in photoheterotrophic condition –> PS is a burden here - sll0689, pxcA, slr1609 - all increased fitness in HC,HL, first two are Na+/CO2 (?) trnasporters, slr1609 we know from before, annotated as fatty acid CoA ligase, but probably it’s something different - sll6055, slr1505, slr1990 - all increased fitness in photoheterotrophic condition, and decreased fitness in HC/LL conditions. Not much is known about these genes, probably a role in photosynthesis, as the pattern is similar to psb genes (PSII maturation?) - slr0813, slr0907, slr909, slr1299 - all increased fitness in HC/LL. Not clear what connects these genes functionally.

11 Differential fitness of non-coding RNAs (ncRNAs)

11.2 Antisense RNAs as regulatory elements

The first part of a more detailed analysis is to extract asRNAs with differential fitness, and compare them to their associated genes. The assumption is that sgRNAs targeting asRNAs in reality repress transcription of their parent genes, and by these means produce a fitness effect that can not be attributed to the action of the asRNA itself. The first step is filter the ncRNA dataset and order ncRNAs by fitness similarity.

df_ncRNA_select <- df_ncRNA %>%
  filter(ncRNA_type != "iTSS", time == 0) %>%
  group_by(sgRNA_target) %>%
  filter(any(score >= 4)) %>% ungroup %>%
  mutate(sgRNA_target = fct_cluster(sgRNA_target, condition, wmean_fitness))
plot_asRNA_heat <- df_ncRNA_select %>% filter(ncRNA_type == "asRNA") %>%
  mutate(wmean_fitness = wmean_fitness %>% replace(., . > 4, 4) %>% replace(., . < -4, -4)) %>%
  ggplot(aes(x = condition, y = sgRNA_target, fill = wmean_fitness)) +
  geom_tile() + custom_theme(legend.pos = "bottom") +
  labs(x = "", y = "") +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1)) +
  scale_fill_gradientn(colours = c(custom_colors[1], grey(0.9), custom_colors[2]),
    limits = c(-4, 4))

# check correlation of asRNA fitness with associated gene fitness
plot_asRNA_xy <- df_ncRNA_select %>% filter(ncRNA_type == "asRNA") %>%
  left_join(by = c("condition", "locus"),
    select(df_gene, locus, condition, wmean_fitness, sd_fitness) %>% distinct %>%
    rename(gene_fitness = wmean_fitness, sd_gene_fitness = sd_fitness)) %>%
  select(locus, condition, wmean_fitness, gene_fitness) %>%
  mutate(locus = if_else(locus %in% c("slr0882", "sll1773", "sml0004", "slr1609", "slr1939"), locus, "other")) %>%
  ggplot(aes(x = wmean_fitness, y = gene_fitness, color = locus)) +
  geom_abline(intercept = 0, slope = 1, lty = 2) +
  geom_abline(intercept = 4, slope = 1, lty = 2) +
  geom_abline(intercept = -4, slope = 1, lty = 2) +
  geom_point() +
  coord_cartesian(xlim = c(-9, 5), ylim = c(-9, 5)) +
  custom_theme(legend.pos = c(0.15, 0.8), legend.key.size = unit(0.3, "cm")) +
  labs(x = "asRNA fitness", y = "gene fitness") +
  scale_color_manual(values = custom_colors[c(5,1:4,6)])

11.3 noncoding RNAs as regulatory elements

The second part of this analysis is to look at non-gene associated (intergenic) ncRNA elements. Of these, several are known to have a regulatory effect.

plot_ncRNA_heat <- df_ncRNA_select %>% filter(ncRNA_type == "ncRNA") %>%
  mutate(wmean_fitness = wmean_fitness %>% replace(., . > 4, 4) %>% replace(., . < -4, -4)) %>%
  ggplot(aes(x = condition, y = sgRNA_target, fill = wmean_fitness)) +
  geom_tile() + custom_theme(legend.pos = "none") +
  labs(x = "", y = "") +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1)) +
  scale_fill_gradientn(colours = c(custom_colors[1], grey(0.9), custom_colors[2]),
    limits = c(-4, 4))

ggarrange(ncol = 3,
  plot_ncRNA_overview,
  ggarrange(nrow = 2, heights = c(0.65, 0.35),
    plot_asRNA_heat, plot_asRNA_xy + theme(plot.margin = unit(c(4,12,16,12), "points"))),
  ggarrange(nrow = 2, heights = c(0.75, 0.25), plot_ncRNA_heat, ggplot() + custom_theme())
)

12 Export summary table of all genes and conditions

Export a summary table of all genes and conditions, so that it’s easy for other people to look up single conditions as for example done in one-by-one fitness comparisons. This is best done in wide format (one column per condition).

df_gene %>% ungroup %>%
  filter(sgRNA_type == "gene") %>%
  select(locus, sgRNA_target, gene_name, condition, wmean_fitness) %>% 
  distinct %>%
  pivot_wider(names_from = condition, values_from = wmean_fitness) %>%
  write_csv("../data/output/fitness_summary.csv")

df_gene %>%
  filter(sgRNA_type == "gene") %>%
  write_csv("../data/output/fitness_genes.csv")

df_kegg %>% write_csv("../data/output/kegg_annotation.csv")

The entire pipeline takes about 25 minutes to run on a standard notebook. To work on single sections, the work space is exported to avoid constant recalculation of result tables.

# remove large intermediate objects
rm("list_condition_pairs")
save(list = ls(), file = "../pipeline/CRISPRi_V2_data_processing.RData")

13 Session Info

sessionInfo()
R version 4.1.2 (2021-11-01)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Linux Mint 20

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               LC_TIME=sv_SE.UTF-8       
 [4] LC_COLLATE=en_US.UTF-8     LC_MONETARY=sv_SE.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=sv_SE.UTF-8       LC_NAME=C                  LC_ADDRESS=C              
[10] LC_TELEPHONE=C             LC_MEASUREMENT=sv_SE.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] grid      stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] fluctuator_0.1.0    ggheatmap_2.1       ggridges_0.5.3      httr_1.4.2          ggpubr_0.4.0       
 [6] kableExtra_1.3.4    corrplot_0.92       limma_3.48.3        KEGGREST_1.32.0     tsne_0.1-3         
[11] vegan_2.5-7         permute_0.9-5       dendextend_1.15.2   scales_1.1.1        latticetools_0.1.0 
[16] latticeExtra_0.6-29 lattice_0.20-45     ggrepel_0.9.1       forcats_0.5.1       stringr_1.4.0      
[21] dplyr_1.0.7         purrr_0.3.4         readr_2.1.1         tidyr_1.1.4         tibble_3.1.6       
[26] ggplot2_3.3.5       tidyverse_1.3.1    

loaded via a namespace (and not attached):
  [1] readxl_1.3.1           backports_1.4.1        systemfonts_1.0.3      plyr_1.8.6            
  [5] splines_4.1.2          GenomeInfoDb_1.26.4    digest_0.6.29          yulab.utils_0.0.4     
  [9] htmltools_0.5.2        viridis_0.6.2          fansi_1.0.2            magrittr_2.0.1        
 [13] cluster_2.1.2          tzdb_0.2.0             Biostrings_2.60.2      modelr_0.1.8          
 [17] vroom_1.5.7            svglite_2.0.0          jpeg_0.1-9             colorspace_2.0-2      
 [21] rvest_1.0.2            haven_2.4.3            xfun_0.29              crayon_1.4.2          
 [25] RCurl_1.98-1.5         jsonlite_1.7.3         Exact_3.1              glue_1.6.0            
 [29] gtable_0.3.0           zlibbioc_1.36.0        XVector_0.30.0         webshot_0.5.2         
 [33] car_3.0-12             BiocGenerics_0.38.0    abind_1.4-5            mvtnorm_1.1-3         
 [37] DBI_1.1.2              rstatix_0.7.0          Rcpp_1.0.8             viridisLite_0.4.0     
 [41] gridGraphics_0.5-1     bit_4.0.4              proxy_0.4-26           stats4_4.1.2          
 [45] RColorBrewer_1.1-2     ellipsis_0.3.2         factoextra_1.0.7       pkgconfig_2.0.3       
 [49] XML_3.99-0.8           farver_2.1.0           sass_0.4.0             dbplyr_2.1.1          
 [53] utf8_1.2.2             ggplotify_0.1.0        tidyselect_1.1.1       labeling_0.4.2        
 [57] rlang_0.4.12           munsell_0.5.0          cellranger_1.1.0       tools_4.1.2           
 [61] cli_3.1.1              generics_0.1.1         broom_0.7.11           evaluate_0.14         
 [65] fastmap_1.1.0          knitr_1.37             bit64_4.0.5            fs_1.5.2              
 [69] rootSolve_1.8.2.3      nlme_3.1-152           aplot_0.1.2            xml2_1.3.3            
 [73] compiler_4.1.2         rstudioapi_0.13        curl_4.3.2             png_0.1-7             
 [77] e1071_1.7-9            ggsignif_0.6.3         reprex_2.0.1           bslib_0.3.1           
 [81] DescTools_0.99.44      stringi_1.7.6          Matrix_1.3-4           vctrs_0.3.8           
 [85] pillar_1.6.4           lifecycle_1.0.1        jquerylib_0.1.4        data.table_1.14.2     
 [89] cowplot_1.1.1          bitops_1.0-7           lmom_2.8               patchwork_1.1.1       
 [93] R6_2.5.1               directlabels_2021.1.13 gridExtra_2.3          IRanges_2.24.1        
 [97] gld_2.6.4              boot_1.3-28            MASS_7.3-54            assertthat_0.2.1      
[101] withr_2.4.3            S4Vectors_0.28.1       GenomeInfoDbData_1.2.4 mgcv_1.8-38           
[105] expm_0.999-6           parallel_4.1.2         hms_1.1.1              quadprog_1.5-8        
[109] ggfun_0.0.5            class_7.3-19           rmarkdown_2.11         carData_3.0-5         
[113] lubridate_1.8.0       
---
title: "CRISPRi library V2, data processing pipeline"
output:
  html_notebook:
    theme: cosmo
    toc: yes
    number_sections: yes
  html_document:
    toc: yes
    df_print: paged
---

----------

# Description

This R notebook details the data processing and visualization for growth competition experiments with a CRISPRi sgRNA library. The library contains around 20,000 unique sgRNA repression mutants tailored for the cyanobacterium _Synechocystis_ sp. PCC6803. This library is the second version (therefore "V2") of an sgRNA library for _Synechocystis_, containing five instead of only two sgRNAs per gene. In some cases, genes or ncRNAs are so short that it is not possible to design a maximum of five individual sgRNAs.

The first iteration of the _Synechocystis_ sgRNA library was [published in Nature Communications, 2020](https://www.nature.com/articles/s41467-020-15491-7).

# Prerequisites

Load required packages.

```{r, message = FALSE }
suppressPackageStartupMessages({
  library(tidyverse)
  library(ggrepel)
  library(lattice)
  library(latticeExtra)
  library(latticetools)
  library(scales)
  library(dendextend)
  library(vegan)
  library(tsne)
  library(KEGGREST)
  library(limma)
  library(corrplot)
  library(kableExtra)
  library(grid)
  library(ggpubr)
})
```

Define global figure style, default colors, and a plot saving function.

```{r, echo = FALSE}
# custom ggplot2 theme that is reused for all later plots
custom_colors = c("#E7298A", "#66A61E", "#E6AB02", "#7570B3", "#B3B3B3", "#1B9E77", "#D95F02", "#A6761D")
custom_range <- function(n = 5) {colorRampPalette(custom_colors[c(1,5,2)])(n)}

custom_theme <- function(base_size = 12, base_line_size = 1.0, base_rect_size = 1.0, ...) {
  theme_light(base_size = base_size, base_line_size = base_line_size, base_rect_size = base_rect_size) + theme(
    title = element_text(colour = grey(0.4), size = 10),
    plot.margin = unit(c(12,12,12,12), "points"),
    axis.ticks.length = unit(0.2, "cm"),
    axis.ticks = element_line(colour = grey(0.4), linetype = "solid", lineend = "round"),
    axis.text.x = element_text(colour = grey(0.4), size = 10),
    axis.text.y = element_text(colour = grey(0.4), size = 10),
    panel.grid.major = element_line(size = 0.6, linetype = "solid", colour = grey(0.9)),
    panel.grid.minor = element_blank(),
    panel.border = element_rect(linetype = "solid", colour = grey(0.4), fill = NA, size = 1.0),
    panel.background = element_blank(),
    strip.background = element_blank(),
    strip.text = element_text(colour = grey(0.4), size = 10, margin = unit(rep(3,4), "points")),
    legend.text = element_text(colour = grey(0.4), size = 10),
    legend.title = element_blank(),
    legend.background = element_blank(),
    ...
  )
}

# set graphical parameter for subfigure labels
list_fontpars <- list(face = "plain", size = 14)

# function to export an image as svg and png
save_plot <- function(pl, path = "../figures/", width = 6, height = 6) {
  pl_name <- deparse(substitute(pl))
  svg(filename = paste0(path, pl_name, ".svg"),
    width = width, height = height)
  print(pl)
  dev.off()
  png(filename = paste0(path, pl_name, ".png"),
    width = width*125, height = height*125, res = 120)
  print(pl)
  invisible(capture.output(dev.off()))
}
```


# Quality control

## Data import

Load raw data. The main table contains already normalized quantification of all sgRNAs, fold change, multiple hypothesis corrected p-values, and fitness score. Contrary to the processing of [our first CRISPRi library V1](https://github.com/m-jahn/R-notebooks), much of the functionality from the notebook was transferred into the [new CRISPRi library pipeline on github](https://github.com/m-jahn/CRISPRi-lib-pipe).

```{r}
# load first seq run
load("../data/input/DESeq2_result.Rdata")
df_main <- DESeq_result_table

# load second seq run
load("../data/input/DESeq2_result_2.Rdata")
df_main <- bind_rows(df_main, DESeq_result_table)

# remove single results table
rm(DESeq_result_table)
```


## Data annotation

Different annotation columns are added to the main data frame, including a short sgRNA identifier (excluding the position on the gene), an sgRNA index (1 to 5), and genome annotation from Uniprot. The Uniprot data is dynamically downloaded for every update of this pipeline using their very simple API (`read_tsv("https://www.uniprot.org/uniprot/?query=taxonomy:1111708&format=tab")`). The full list of columns that can be queried is available [here](https://www.uniprot.org/help/uniprotkb_column_names).
Pathway annotation from KEGG is later in the pipeline added using the `KEGGREST` package.

```{r, message = FALSE}
df_main <- df_main %>%
  # correct an error in sgRNA naming
  mutate(sgRNA = gsub('”', '2', sgRNA)) %>%
  # split sgRNA names into target gene and position
  separate(sgRNA, into = c("sgRNA_target", "sgRNA_position"), sep = "\\|",
    remove = FALSE) %>%
  
  # add sgRNA index number (1 to maximally 5) and type
  group_by(sgRNA_target) %>%
  mutate(
    sgRNA_position = as.numeric(sgRNA_position),
    sgRNA_index = sgRNA_position %>% as.factor %>% as.numeric,
    sgRNA_type = if_else(grepl("^nc_", sgRNA), "ncRNA", "gene")) %>%
  ungroup %>%
  
  # map trivial names to LocusTags using a manually curated list
  left_join(
    read_tsv("../data/input/mapping_trivial_names.tsv", col_types = cols()),
    by = c("sgRNA_target" = "gene")) %>%
  
  # remove some empty rows (NA targets)
  filter(!is.na(sgRNA_target)) %>%
  
  # remove 2 conditions without response
  filter(!condition %in% c("BG11", "LC, 200uE")) %>%
  
  # split condition into separate cols
  separate(condition, into = c("carbon", "light", "treatment_1", "treatment_2"),
    sep = ", ", remove = FALSE, fill = "right") %>%
  unite("treatment", treatment_1, treatment_2, sep = ", ", na.rm = TRUE)
```

Overview about the different conditions.

```{r}
df_cultivation_summary <- df_main %>% group_by(condition) %>%
  summarize(
    time_points = paste(unique(time), collapse = ", "),
    carbon = unique(carbon),
    light = unique(light),
    treatment = unique(treatment),
    min_fit = min(fitness),
    med_fit = median(fitness),
    max_fit = max(fitness))

print(df_cultivation_summary)
write_csv(df_cultivation_summary, file = "../data/output/cultivation_summary.csv")
```

Retrieve gene info from uniprot and merge with main data frame. We need to make a custom function to retrieve and parse the data from uniprot, because of a bug in the security level on Ubuntu 20.04. The fallback option is to load a local copy of uniprot annotation for this organism.

```{r}
library(httr)
uniprot_url <- paste0(
   "https://www.uniprot.org/uniprot/?query=taxonomy:1111708&format=tab&",
   "columns=id,genes,genes(PREFERRED),protein_names,length,mass,ec,database(KEGG)")

get_uniprot <- function(url) {
  # reset security level, caused by a faulty SSL certificate on server side,
  # see this thread: https://github.com/Ensembl/ensembl-rest/issues/427
  httr_config <- config(ssl_cipher_list = "DEFAULT@SECLEVEL=1")
  res <- with_config(config = httr_config, GET(url))
  server_error = simpleError("")
  df_uniprot <- tryCatch(
    read_tsv(content(res), col_types = cols()),
    error = function(server_error) {
      message("Uniprot server not available, falling back on local Uniprot DB copy")
      read_tsv("../data/input/uniprot_synechocystis.tsv", col_types = cols())
    }
  )
}

df_uniprot <- get_uniprot(uniprot_url) %>%
  rename_with(tolower) %>%
  rename(locus = `cross-reference (kegg)`, gene_name = `gene names`,
    gene_name_short = `gene names  (primary )`, ec_number = `ec number`,
    protein = `protein names`, uniprot_ID = entry
  ) %>%
  separate_rows(locus, sep = ";syn:") %>%
  mutate(locus = str_remove_all(locus, "syn:|;")) %>%
  filter(!is.na(locus))

df_main <- left_join(df_main, filter(df_uniprot, !duplicated(locus)),
  by = "locus")
```


## Number of sgRNAs

Each gene is represented by up to five sgRNAs. We can test if all or only some of the 5 sgRNAs are "behaving" in the same way in the same conditions, more mathematically speaking we can estimate the correlation of every sgRNA with another. First let's summarize how many genes have 5, 4, 3 sgRNAs and so on associated with them.

```{r, , fig.width = 6, fig.height = 3.5}
# N unique sgRNAs in dataset
paste0("Number of unique sgRNAs: ", unique(df_main$sgRNA) %>% length)

# N genes with 1,2,3,4 or 5 sgRNAs
plot_sgRNAs_per_gene <- df_main %>%
  group_by(sgRNA_type, sgRNA_target) %>%
  summarize(n_sgRNAs = length(unique(sgRNA_position)), .groups = "drop_last") %>%
  count(n_sgRNAs) %>% filter(n_sgRNAs <= 5) %>%
  ggplot(aes(x = factor(n_sgRNAs, 5:1), y = n, label = n)) +
  geom_col(show.legend = FALSE) +
  geom_text(size = 3, nudge_y = 200, color = grey(0.5)) +
  facet_grid(~ sgRNA_type) +
  labs(x = "n sgRNAs / target", y = "n targets") +
  coord_cartesian(ylim = c(-50, 3500)) +
  custom_theme()

print(plot_sgRNAs_per_gene)
save_plot(plot_sgRNAs_per_gene, width = 6, height = 3.5)
```

# Normalization

## Fitness distribution of all conditions

Before biological analysis continues, we need to check if fitness (and log2 FC from which it is calculated) is equally distributed. For example, strictly essential genes like ribosomal genes should show the same degreee of depletion over time, regardless of condition.

We can compare fitness over all conditions using a scatter plot matrix. We can see that some conditions are very similar to each other, for example the conditions treated with glucose (`LC, LL +g`, `LC, LL, +D, +G`, `HC, LL +g`). Others are more dissimilar to the rest, for example `LC, IL` and `LC, LL, +FL`. They are more alike each other, although `LC, LL, +FL` should be more comparable to `LC, LL`, hinting at experimental bias. In this case both of these conditions (and `LC, LL, +G`) were pre-cultivated in low light instead of high light, as opposed to the rest of the samples.

```{r, fig.width = 8, fig.height = 8}
df_main %>% filter(time == 0, sgRNA_index == 1) %>%
  select(locus, condition, fitness) %>%
  filter(!is.na(locus)) %>%
  pivot_wider(names_from = condition, values_from = fitness) %>%
  select(-locus) %>%
  custom_splom(pch = 19, cex = 0.3, col = grey(0.4, 0.4), pscales = 0)
```

## Normalization strategy

In order to account for experimental or quantification bias, we can try to normalize the log2 FC distribution between all samples, and then re-calculate fitness. The underlying assumption is that e.g. essential genes should deplete at the same rate and hence show identical log2 FC at identical time points. Different types of experimental bias influence global fitness distribution and should be reduced with normalization. Here we try a 'cyclic loess' or quantile normalization that gave good results in a quick comparison.

```{r, fig.width = 6, fig.height = 5}
# construct a normalization function that takes three colums as input,
# the numeric variable to be normalized, the conditioning variable
# (character or factor), and an ID that identifies each observation (sgRNA)
apply_norm = function(id, cond, var) {
  df_orig <- tibble(id = id, cond = cond, var = var)
  df_new <- pivot_wider(df_orig, names_from = cond, values_from = var) %>%
  column_to_rownames("id") %>% as.matrix %>%
  limma::normalizeBetweenArrays(method = "quantile") %>%
  as_tibble(rownames = "id") %>%
  pivot_longer(-id, names_to = "cond", values_to = "var_norm")
  left_join(df_orig, df_new, by = c("id", "cond")) %>% pull(var_norm)
}

# apply normalization
df_main <- df_main %>%
  mutate(FoldChange = 2^log2FoldChange) %>%
  group_by(time) %>%
  mutate(
    FoldChange_norm = apply_norm(sgRNA, condition, FoldChange),
    log2FoldChange = log2(FoldChange_norm)
  ) %>% ungroup

# compare effect of normalization
df_main %>% group_by(condition) %>% slice(1:10000) %>%
  ggplot(aes(x = log2(FoldChange), y = log2(FoldChange_norm), color = factor(time))) +
  geom_point(size = 0.5) +
  facet_wrap(~ condition, ncol = 4) +
  custom_theme() +
  scale_color_manual(values = custom_colors)
```

Another way to look at the result of the normalization is to compare the global distribution of log2 FC values, as a density plot.

```{r, fig.width = 6, fig.height = 5, warning = FALSE}
library(ggridges)
df_main %>% filter(time == 10) %>%
  select(sgRNA, condition, FoldChange, FoldChange_norm) %>%
  pivot_longer(matches("^Fold"), names_to = "metric", values_to = "FC") %>%
  distinct %>%
  ggplot(aes(x = log2(FC), y = condition, group = condition)) + 
  geom_density_ridges(fill = "#00AFBB99", col = grey(0.4)) +
  facet_wrap(~ metric, ncol = 4) +
  lims(x = c(-2, 1.5)) +
  custom_theme()
```

Now we need to re-calculate fitness based on the normalized log2 FC.

```{r}
df_main <- df_main %>%
  select(-FoldChange, -FoldChange_norm) %>%
  group_by(sgRNA, condition) %>%
  mutate(fitness = DescTools::AUC(time, log2FoldChange)/(max(time)/2)) %>%
  arrange(sgRNA_target, sgRNA_index, condition, time)
```

# Fitness score aggregation

## Correlation of sgRNAs

Different methods can be used to estimate similarity between samples (sgRNAs). For example, factor analysis is a method to dissect underlying sources of variation within the dataset, and the contribution to overall variation. The most famous example is principal component analysis (PCA). We can also use the correlation coefficient of sgRNAs to each other to see if one of the sgRNAs contributes stronger to overall variation.

This is an example of an apparently strictly essential gene, encoding the ribosomal protein `rps10`. Most of the sgRNA repressor strains are depleted, the correlation between sgRNAs is high. The strength of depletion varies though, and the strain with sgRNA 3 is not depleted at all. We want to give higher weights to sgRNAs that correlate well with each other, and/or show stronger effect (depletion/enrichment).

```{r, fig.width = 7, fig.height = 5.5}
plot_sgRNA_ribo_example <- df_main %>% filter(sgRNA_target == "rps10") %>%
  mutate(sgRNA_index = factor(sgRNA_index, 1:5)) %>%
  ggplot(aes(x = time, y = log2FoldChange, color = sgRNA_index)) +
  geom_line(size = 1) + geom_point(size = 2) +
  facet_wrap(~ condition, ncol = 4) +
  custom_theme() +
  scale_color_manual(values = custom_range(5))

print(plot_sgRNA_ribo_example)
save_plot(plot_sgRNA_ribo_example, width = 7, height = 5.5)
```

A correlation score can be calculated by computing the correlation coefficient of all sgRNAs to each other. This score is robustly summarized by taking the median, and rescaling it from the respective minima and maxima [-1, 1] to [0, 1]. This score serves as a weight component for each sgRNA to calculate the (global) weighted mean of log2 FC over all sgRNAs. The score has the characteristic that it gives a weight of 1 for an sgRNA perfectly correlated with all other sgRNAs of the same gene, and a weight of 0 for sgRNAs perfectly anti-correlated to the other sgRNAs.

For a matrix of $x = 1 .. m$ sgRNAs and $y = 1 .. n$ observations (measurements), the correlation $R$ of one sgRNA to another is calculated using Pearson's method:

$R_x=cor([log_2FC_{x1,y1} ... log_2FC_{x1,yn}], [log_2FC_{x2,y1} ... log_2FC_{x2,yn}])$

The correlation weight of one sgRNA is then calculated as median of all $R$ rescaled between 0 and 1.

$w_x = \frac{1 + median(R_1, R_2, ..., R_m)}{2}$

The following example shows the correlation matrix for the 5 `rps10` sgRNAs, and their weights. The self correlation of each sgRNA (R = 1) is removed prior to weight determination.

```{r, fig.width = 4, fig.height = 4}
cor_matrix <- df_main %>% filter(sgRNA_target == "rps10") %>% ungroup %>%
  select(sgRNA_index, log2FoldChange, condition, time) %>%
  pivot_wider(names_from = c("condition", "time"), values_from = log2FoldChange) %>%
  arrange(sgRNA_index) %>% column_to_rownames("sgRNA_index") %>%
  as.matrix %>% t %>% cor(method = "pearson")

weights <- cor_matrix %>% replace(., . == 1, NA) %>%
  apply(2, function(x) median(x, na.rm = TRUE)) %>%
  rescale(from = c(-1, 1), to = c(0, 1))

# plot heatmap
lattice::levelplot(cor_matrix %>% replace(., . == 1, NA),
  col.regions = custom_range(20))

# print weights
weights
```

----------

Now we can create a function that will compute weights for all sgRNAs, and add the weights to the data set.

```{r, warning = FALSE}
determine_corr <- function(index, value, condition, time) {
  # make correlation matrix
  df <- data.frame(index = index, value = value, condition = condition, time = time)
  cor_matrix <- pivot_wider(df, names_from = c("condition", "time"), values_from = value) %>%
    arrange(index) %>% column_to_rownames("index") %>%
    as.matrix %>% t %>% cor(method = "pearson")
  
  # determine weights
  weights <- cor_matrix %>% replace(., . == 1, NA) %>%
    apply(2, function(x) median(x, na.rm = TRUE)) %>%
    scales::rescale(from = c(-1, 1), to = c(0, 1)) %>%
    enframe("index", "weight") %>% mutate(index = as.numeric(index)) %>%
    mutate(weight = replace(weight, is.na(weight), 1))
  
  # return vector of weights the same order and length 
  # as sgRNA index vector
  left_join(df, weights, by = "index") %>% pull(weight)
}

df_main <- df_main %>%
  group_by(sgRNA_target) %>%
  mutate(sgRNA_correlation = determine_corr(sgRNA_index,
    log2FoldChange, condition, time))
```


## Efficiency of sgRNAs

The correlation of each sgRNA with each other is a "global" parameter as it is identical over all conditions. A second global parameter, **sgRNA efficiency**, can be obtained using a similar approach. We expect that fitness of all sgRNAs for one gene is not normally distributed because sgRNAs are not ideal replicate measurements. They are biased by position effects and off-target binding, see [Wang et al., Nature Comms, 2018](https://www.nature.com/articles/s41467-018-04899-x) for a very insightful and comprehensive analysis of the number and position of sgRNAs required to estimate gene fitness. 

We calculate sgRNA efficiency $E$ as the median absolute fitness (AUC of log2FC over time) of an sgRNA $x = 1 .. m$ over all observations [conditions] $y = 1 .. n$.

$E_x=median(abs(fitness_{x1, y1}, fitness_{x1, y2}, ..., fitness_{x1, yn}))$

To normalize between all sgRNAs, $E$ is rescaled to a range between 0 and 1.

$E_x=\frac{E_x}{max(E_1, E_2, ..., E_m)}$

```{r}
df_main <- df_main %>% group_by(sgRNA_target) %>%
  mutate(sgRNA_efficiency = ave(fitness, sgRNA_index, FUN = function(x) median(abs(x))) %>%
    {./max(.)})
```

This is the resulting sgRNA efficiency for the example gene above, `rps10`.

```{r}
df_main %>% filter(sgRNA_target == "rps10") %>% ungroup %>%
  select(sgRNA_index, sgRNA_efficiency) %>% distinct %>% 
  arrange(sgRNA_index) %>% deframe
```


## Position bias of sgRNA repression

Plot the **weight of each sgRNA** to see if there is a dependency between correlation and sgRNA position. There is no significant trend.

We can also quantify how many genes have strongly correlated sgRNAs and how many have outliers. In order to do this, the median weight of the (up to) 5 sgRNAs per gene is plotted. Generally, the median weight ranges between 0.5 and 1.0, showing on average good correlation.

```{r, fig.width = 6, fig.height = 3}
plot_sgRNA_correlation <- df_main %>%
  select(sgRNA_target, sgRNA_index, sgRNA_correlation) %>%
  filter(sgRNA_index <= 5) %>%
  distinct %>%
  # plot
  ggplot(aes(x = factor(sgRNA_index), y = sgRNA_correlation)) +
  geom_boxplot(outlier.shape = "") +
  labs(x = "sgRNA position", y = "correlation") +
  stat_summary(fun.data = function(x) c(y = median(x)+0.07, 
    label = round(median(x), 2)), geom = "text", size = 3) +
  stat_summary(fun.data = function(x) c(y = 1.1, 
    label = length(x)), geom = "text", color = grey(0.5), size = 3) +
  coord_cartesian(ylim = c(-0.15, 1.15)) +
  custom_theme()

plot_sgRNA_correlation_hist <- df_main %>%
  select(sgRNA_target, sgRNA_index, sgRNA_correlation) %>%
  filter(sgRNA_index <= 5) %>%
  distinct %>% group_by(sgRNA_target) %>%
  summarize(
    median_sgRNA_correlation = median(sgRNA_correlation),
    min_sgRNA_correlation = min(sgRNA_correlation)
  ) %>%
  # plot
  ggplot(aes(x = median_sgRNA_correlation)) +
  geom_histogram(bins = 40, fill = custom_colors[1], alpha = 0.7) +
  custom_theme()

save_plot(plot_sgRNA_correlation_hist, width = 5, height = 4)
save_plot(plot_sgRNA_correlation, width = 5, height = 4)
ggarrange(plot_sgRNA_correlation, plot_sgRNA_correlation_hist, ncol = 2)
```

Second, the binding position of the sgRNAs could be correlated to the strength of repression. In other words sgRNAs binding closer to the promoter could have stronger ability to repress a gene, see Figure 1 B in [Wang et al., Nature Comms, 2018](https://www.nature.com/articles/s41467-018-04899-x). We plot **sgRNA efficiency** for genes only, because the absolute majority of those has 5 sgRNAs.

```{r, fig.width = 6, fig.height = 3}
plot_sgRNA_efficiency <- df_main %>%
  filter(sgRNA_index <= 5, sgRNA_type == "gene") %>%
  select(sgRNA_target, sgRNA_index, sgRNA_efficiency) %>% distinct %>%
  ggplot(aes(x = factor(sgRNA_index), y = sgRNA_efficiency)) +
  geom_boxplot(notch = FALSE, outlier.shape = ".") +
  labs(x = "sgRNA position (relative)", y = "repression efficiency") +
  coord_cartesian(ylim = c(-0.15, 1.15)) +
  stat_summary(fun.data = function(x) c(y = median(x)+0.07, 
    label = round(median(x), 2)), geom = "text", size = 3) +
  stat_summary(fun.data = function(x) c(y = 1.1, 
    label = length(x)), geom = "text", color = grey(0.5), size = 3) +
  custom_theme()


plot_sgRNA_efficiency_hist <- df_main %>%
  filter(sgRNA_index <= 5, sgRNA_type == "gene") %>%
  select(sgRNA_target, sgRNA_position, sgRNA_efficiency) %>% distinct %>%
  group_by(sgRNA_position) %>%
  summarize(sgRNA_efficiency = median(sgRNA_efficiency), n_pos = n()) %>%
  filter(n_pos >= 10) %>%
  ggplot(aes(x = sgRNA_position, y = sgRNA_efficiency)) +
  labs(x = "sgRNA position (nt)", y = "repression efficiency") +
  geom_point(col = alpha(custom_colors[5], 0.5)) +
  geom_smooth() +
  custom_theme()

save_plot(plot_sgRNA_efficiency, width = 5, height = 4)
save_plot(plot_sgRNA_efficiency_hist, width = 5, height = 4)
ggarrange(plot_sgRNA_efficiency, plot_sgRNA_efficiency_hist, ncol = 2)
```

Export draft **Figure 1** for manuscript.

```{r, fig.width = 7, fig.height = 5.5}
plot_selected_sgRNAs <- df_main %>%
  filter(
    grepl("ctrl[1-5]$|rps10$", sgRNA_target), 
    condition %in% c("HC, HL", "HC, LL", "LC, IL", "LC, LL")) %>%
  mutate(
    sgRNA_index2 = as.numeric(str_extract(sgRNA_target, "[1-9]$")),
    sgRNA_index = case_when(sgRNA_position == 0 ~ sgRNA_index2, TRUE ~ sgRNA_index),
    sgRNA_target = str_extract(sgRNA_target, "[a-zA-Z]*")
  ) %>%
  ggplot(aes(x = time, y = log2FoldChange, color = factor(sgRNA_index))) +
  geom_line(size = 1) + geom_point(size = 2) +
  facet_grid(sgRNA_target ~ condition) +
  custom_theme(legend.position = 0) +
  coord_cartesian(ylim = c(-4.5, 2.5)) +
  scale_color_manual(values = custom_range(5))

svg(filename = "../figures/figure1.svg", width = 7, height = 5.5)
ggarrange(ncol = 2, nrow = 2, widths = c(0.6, 0.4), labels = LETTERS[1:4], font.label = list_fontpars,
  plot_sgRNAs_per_gene + theme(plot.margin = unit(c(12,12,12,12), "points")),
  plot_sgRNA_efficiency + theme(plot.margin = unit(c(26,12,12,12), "points")),
  plot_selected_sgRNAs + theme(plot.margin = unit(c(12,-4,12,14), "points")),
  plot_sgRNA_correlation + theme(plot.margin = unit(c(26,12,12,12), "points"))
)
dev.off()
```

Export **supplemental figure with all ribosomal genes** (rps*NN*/rpl*NN*).

```{r, fig.width = 7, fig.height = 10}
plot_sgRNAs_ribosome <- df_main %>%
  filter(str_detect(sgRNA_target, "rp[sl][0-9]*$")) %>%
  filter(condition == "LC, LL") %>%
  ggplot(aes(x = time, y = log2FoldChange, color = factor(sgRNA_index))) +
  geom_line(size = 1) + geom_point(size = 2) +
  facet_wrap(~ sgRNA_target, ncol = 7) +
  custom_theme(legend.position = "top") +
  scale_color_manual(values = custom_range(5))

print(plot_sgRNAs_ribosome)
```


# Gene fitness calculation

## Summarize sgRNA fitness to gene fitness

With the correlation and the efficiency per sgRNA, we can compute the **weighted mean of all sgRNAs**. For comparison, we also test simple strategies such as the standard **arithmetic mean** and a top 1 and top 2 sgRNAs strategy. Metrics are calculated for log2 FC, and fitness.

```{r, warning = FALSE, message = FALSE}
df_controls <- df_main %>% ungroup %>% 
  filter(str_detect(sgRNA_target, "ctrl[0-9]+$"))

df_gene <- df_main %>%
  
  # keep all annotation columns
  group_by(sgRNA_target, sgRNA_type, locus, gene_name, condition, 
    carbon, light, treatment, time) %>%
  
  # summarize FC and fitness...
  summarize(.groups = "drop",
    
    # log2 FC
    mean_log2FoldChange = mean(log2FoldChange),
    wmean_log2FoldChange = weighted.mean(log2FoldChange, sgRNA_correlation * sgRNA_efficiency),
    top1_log2FoldChange = log2FoldChange[which.max(sgRNA_efficiency)],
    top2_log2FoldChange = mean(log2FoldChange[order(sgRNA_efficiency, decreasing = TRUE)[1:2]]),
    sd_log2FoldChange = sd(log2FoldChange),
    
    # fitness
    mean_fitness = mean(fitness),
    wmean_fitness = weighted.mean(fitness, sgRNA_correlation * sgRNA_efficiency),
    top1_fitness = fitness[which.max(sgRNA_efficiency)],
    top2_fitness = mean(fitness[order(sgRNA_efficiency, decreasing = TRUE)[1:2]]),
    sd_fitness = sd(fitness),
    
    # apply significance test, Mann-Whitney U test
    p_value = wilcox.test(fitness, filter(df_controls, condition == unique(condition))$fitness)$p.value
  )
```

Since statistical significance is tested for many genes in parallel, the p-value obtained from MWU test should be multiple-hypothesis corrected. For this purpose we use the Benjamini-Hochberg method. We also calculate a score taking both effect size and p-value into account, according to the publication from [Wang et al., Nat Comm, 2018](http://dx.doi.org/10.1038/s41467-018-04899-x). This score is simply the absolute fitness score multiplied by the negative log10 p-value.


```{r}
df_gene <- df_gene %>%
  group_by(condition, time) %>%
  mutate(
    p_value_adj = p.adjust(p_value, method = "BH"),
    score = abs(wmean_fitness)*-log10(p_value_adj)
  ) %>% ungroup
```


A comparison of log2 FC aggregated by the different method shows clear differences. For the example gene `rps10` the weighted mean and the top method give similar results, representative of the stronger influence from highly depleted sgRNA repression strains. The regular mean is robust, but "shallow", probably underestimating the real effect n fitness. The top 1 method simply picks the most depleted/enriched sgRNA (over all conditions) as representative.

```{r, fig.width = 7, fig.height = 5.5}
df_gene %>% filter(sgRNA_target == "rps10") %>%
  pivot_longer(cols = matches("[n12]_log2FoldChange"), 
    names_to = "metric", values_to = "log2FoldChange") %>%
  mutate(metric = str_remove(metric, "_log2FoldChange")) %>%
  ggplot(aes(x = time, y = log2FoldChange, 
    ymin = log2FoldChange-sd_log2FoldChange, 
    ymax = log2FoldChange+sd_log2FoldChange, color = fct_inorder(metric))) +
  geom_line(size = 1) + geom_point(size = 2) + geom_linerange(size = 1) +
  facet_wrap(~ condition, ncol = 4) +
  custom_theme(legend.position = "right") +
  coord_cartesian(ylim = c(-3.75, 0.75)) +
  scale_color_manual(values = custom_range(4))
```


This plot shows a comparison of the 4 methods for the first 36 genes by alphabetical order, for one selected condition only (1% CO2, BG11, 1,000 µmol photons m-1 s-1). Here we can see that the top1 method is often but not always representative for the gene: For apcD or apcF, it does not seem representative compared to the mean, weighted mean, and top2 methods.

```{r, fig.width = 9, fig.height = 9}
df_gene %>% filter(
    gene_name %in% unique(.data[["gene_name"]])[1:36],
    condition == "HC, HL"
  ) %>%
  pivot_longer(cols = matches("[n12]_log2FoldChange"), names_to = "metric", values_to = "log2FoldChange") %>%
  mutate(metric = str_remove(metric, "_log2FoldChange")) %>%
  ggplot(aes(x = time, y = log2FoldChange, 
    ymin = log2FoldChange-sd_log2FoldChange,
    ymax = log2FoldChange+sd_log2FoldChange, color = fct_inorder(metric))) +
  geom_line(size = 1) + geom_point(size = 2) + geom_linerange(size = 1) +
  facet_wrap(~ sgRNA_target, ncol = 7) +
  custom_theme(legend.position = "top") +
  coord_cartesian(ylim = c(-5, 5)) +
  scale_color_manual(values = custom_range(4))
```

## Global distribution of gene fitness

Global distribution of weighted mean fitness for all genes. Effect of ncRNA repression seems to be much lower than effect of gene repression.

```{r, fig.width = 7, fig.height = 5}
plot_all_fitness_hist <- df_gene %>% filter(time == 0) %>%
  ggplot(aes(x = wmean_fitness, fill = sgRNA_type)) +
  geom_histogram(bins = 100) +
  coord_cartesian(xlim = c(-4, 4), ylim = c(0, 1000)) +
  facet_wrap( ~ condition, ncol = 4) +
  custom_theme() +
  scale_fill_manual(values = custom_colors[c(3:4)])

print(plot_all_fitness_hist)
save_plot(plot_all_fitness_hist, width = 7, height = 5)
```

## Gene fitness vs significance

```{r, fig.width = 7, fig.height = 3.5}
plot_all_fitness_volc <- df_gene %>% filter(time == 0,
    condition %in% c("HC, HL", "LC, LL")) %>%
  arrange(sgRNA_type) %>%
  ggplot(aes(x = wmean_fitness, y = -log10(p_value_adj), col = sgRNA_type)) +
  geom_point(alpha = 0.5, size = 0.5) +
  geom_line(data = data.frame(x = c(seq(-8, -0.5, 0.1), seq(0.5, 8, 0.1)),
    y = 4/c(seq(8, 0.5, -0.1), seq(0.5, 8, 0.1))),
    aes(x = x, y = y, shape = NULL, col = NULL), lty = 2) +
  coord_cartesian(xlim = c(-7, 7), ylim = c(0, 4)) +
  custom_theme(aspect = 1, legend.position = "left", legend.key.size = unit(0.4, "cm")) +
  facet_wrap(~ condition) +
  labs(x = "fitness", y = expression("-log"[10]*" p-value")) +
  scale_color_manual(values = custom_colors[3:4]) +
  scale_shape_manual(values=c(1, 19))

print(plot_all_fitness_volc)
save_plot(plot_all_fitness_volc, width = 6, height = 3)
```

## Behavior of control sgRNAs

Ten sgRNAs were included in the library that have no gene-specific targets. The following plot shows that these negative controls do not have an effect on strain fitness, except probably 2 sgRNAs in one specific condition.

```{r, fig.width = 7, fig.height = 5.5}
plot_controls_sgRNAs <- df_main %>% filter(grepl("ctrl", sgRNA_target)) %>%
  ggplot(aes(x = time, y = log2FoldChange, color = sgRNA_target)) +
  geom_line(size = 1) + geom_point(size = 2) + ylim(-5, 5) +
  facet_wrap(~ condition, ncol = 4) +
  custom_theme() +
  scale_color_manual(values = custom_range(10))

print(plot_controls_sgRNAs)
save_plot(plot_controls_sgRNAs, width = 7, height = 5.5)
```

# Gene enrichment

To plot gene fitness for the enzymes of central carbon metabolism, we need a complete list of enzymes and the genes that they are mapped to. To list the different **KEGG databases** that can be queried, use `listDatabases()`. Gene-pathway mappings are obtained and merged with pathway names and gene/enzyme names.


```{r}
# get mapping of pathways for each gene
df_kegg <- keggLink("pathway", "syn") %>%
  enframe(name = "locus", value = "kegg_pathway_id") %>%
  
  # get list of pathways with name/ID pairs
  left_join(by = "kegg_pathway_id",
    keggList("pathway", "syn") %>%
    enframe(name = "kegg_pathway_id", value = "kegg_pathway")
  ) %>%
  
  # get list of gene/enzyme names
  left_join(by = "locus",
    keggList("syn") %>%
    enframe(name = "locus", value = "kegg_gene") %>%
    mutate(kegg_gene_short = str_extract(kegg_gene, "^[a-zA-Z0-9]*;") %>% 
      str_remove(";"))
  ) %>%
  
  # trim useless prefixes
  mutate(
    locus = str_remove(locus, "syn:"),
    kegg_pathway_id = str_remove(kegg_pathway_id, "path:"),
    kegg_pathway = str_remove(kegg_pathway, " - Synechocystis sp. PCC 6803")
  )

head(df_kegg)
```

## Fitness per pathway

Sometimes even small effects in fitness can be relevant if several genes of the same pathway (or iso-enzymes) are affected. A simple fitness threshold will not reveal those changes. In such cases a more nuanced approach can be taken, a gene set enrichment analysis (GSEA). Several packages exist to test if functionally related genes are enriched, depleted, or both at the same time / the same conditions.

Before we test for enrichment of associated pathways/GO terms, we can have a look at the general depletion/enrichment per KEGG pathway. The fitness distribution per pathway can be visualized using a violin- or scatter plot.

```{r, fig.width = 5.5, fig.height = 5.5}
plot_median_fitness_kegg <- df_gene %>% filter(time == 0) %>%
  inner_join(df_kegg, by = "locus") %>%
  group_by(kegg_pathway, condition) %>%
  summarize(.groups = "drop",
    fitness = median(wmean_fitness),
    n_genes = n()
  ) %>% filter(n_genes >= 20) %>%
  mutate(kegg_pathway = paste0(str_sub(kegg_pathway, 1, 25), "..")) %>%
  mutate(kegg_pathway = fct_reorder(kegg_pathway, fitness, .desc = TRUE)) %>%
  
  ggplot(aes(x = fitness, y = kegg_pathway)) +
  geom_boxplot(outlier.shape = NULL, color = grey(0.5), fill = grey(0.9)) +
  geom_point(aes(color = condition)) +
  geom_vline(xintercept = 0, lty = 2, color = grey(0.5)) +
  labs(x = "median fitness", y = "") +
  custom_theme(legend.position = c(0.25, 0.25), legend.key.size = unit(0.4, "cm")) +
  scale_fill_manual(values = custom_range(11)) +
  scale_color_manual(values = custom_range(11))

print(plot_median_fitness_kegg)
```

Export draft **Figure 2** for manuscript.
We add photosystem I and II genes as examples for differential depletion. A heatmap.

```{r, fig.width = 7, fig.height = 4}
plot_sgRNAs_ps1 <- df_gene %>%
  filter(str_detect(sgRNA_target, "psa[A-Z]*"), time == 0) %>%
  mutate(wmean_fitness = wmean_fitness %>% replace(., . > 4, 4) %>% replace(., . < -4, -4)) %>%
  ggplot(aes(x = condition, y = fct_rev(sgRNA_target), fill = wmean_fitness)) +
  geom_tile() + custom_theme() +
  labs(title = "Photosystem I", x = "", y = "") +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1)) +
  scale_fill_gradientn(colours = c(custom_colors[1], grey(0.9), custom_colors[2]),
    limits = c(-4, 4))

plot_sgRNAs_ps2 <- df_gene %>%
  filter(str_detect(sgRNA_target, "psb[A-Z]*"), time == 0) %>%
  mutate(wmean_fitness = wmean_fitness %>% replace(., . > 4, 4) %>% replace(., . < -4, -4)) %>%
  mutate(sgRNA_target = str_replace(sgRNA_target, "psb13", "psbW")) %>%
  ggplot(aes(x = condition, y = fct_rev(sgRNA_target), fill = wmean_fitness)) +
  geom_tile() + custom_theme() +
  labs(title = "Photosystem II", x = "", y = "") +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1)) +
  scale_fill_gradientn(colours = c(custom_colors[1], grey(0.9), custom_colors[2]),
    limits = c(-4, 4))

ggarrange(ncol = 2, plot_sgRNAs_ps1, plot_sgRNAs_ps2)
```

```{r, fig.width = 8, fig.height = 7}
svg(filename = "../figures/figure2.svg", width = 8, height = 7)
ggarrange(ncol = 2, widths = c(0.65, 0.35),
  ggarrange(nrow = 2, heights =  c(0.34, 0.66), labels = LETTERS[1:2], font.label = list_fontpars,
    plot_all_fitness_volc + theme(plot.margin = unit(c(14,-8,14,40), "points")),
    plot_median_fitness_kegg + theme(plot.margin = unit(c(6,12,12,12), "points"))),
  ggarrange(nrow = 2, heights =  c(0.4, 0.6), labels = LETTERS[3:4], font.label = list_fontpars,
    plot_sgRNAs_ps1 + theme(plot.margin = unit(c(12,0,-14,0), "points")),
    plot_sgRNAs_ps2 + theme(plot.margin = unit(c(12,0,0,0), "points"))
  )
)
dev.off()
```

## Gene enrichment analysis (KEGG)

We use the functions `kegga` for KEGG enrichment analysis and `goana` for GO term enrichment from the `limma` package. Both functions test for over or under-representation of genes associated with certain pathways or GO terms. The functions don't take the strength of differential fitness into account (DF; the depletion/enrichment over time).


```{r}
df_kegg_enrichment <- lapply(unique(df_gene$condition), function(cond) {
  df_gene %>% filter(
  sgRNA_type == "gene", time == 0,
  condition == cond) %>%
  
  # filter for differential fitness (DF) genes
  filter(!between(wmean_fitness, -2.0, 2.0), !is.na(locus)) %>%
  
  # perform KEGG enrichment
  pull(locus) %>% kegga(species.KEGG = "syn") %>%
  mutate(condition = cond)
}) %>% bind_rows

head(df_kegg_enrichment)
```

Now we visualize the pathways that are most enriched for DF genes. It turns out that ribosomal proteins are extremely depleted and therefore score high on the negative log10 p-value for pathway enrichment.

```{r, fig.width = 7, fig.height = 5.5}
df_kegg_enrichment %>%
  rename(kegg_pathway = Pathway) %>%
  group_by(kegg_pathway) %>% filter(N >= 20) %>%
  select(kegg_pathway, condition, P.DE) %>%
  mutate(log10_p_value = -log10(P.DE), .keep = "unused") %>%
  mutate(kegg_pathway = paste0(str_sub(kegg_pathway, 1, 25), "..")) %>%
  
  # make correlation plot
  pivot_wider(names_from = condition, values_from = log10_p_value) %>%
  column_to_rownames(var = "kegg_pathway") %>% as.matrix %>%
  corrplot(is.corr = FALSE, tl.col = grey(0.5), tl.cex = 0.8,
    col = colorRampPalette(custom_colors[c(1,5,2)])(10), col.lim = c(0, 20))
```


# Unsupervised clustering of genes

## Cluster genes by similarity

We can devise a generalized `tidyverse` friendly function to cluster a name variable by a value, grouped by one or more grouping variables. For example, cluster genes (name) by fitness (value) over several conditions (groups). The output is a factor with re-ordered levels.

```{r}
fct_cluster <- function(variable, group, value, method = "ward.D2") {
  df <- tibble(variable = variable, group = group, value = value)
  df <- pivot_wider(df, names_from = group, values_from = value)
  mat <- as.matrix(column_to_rownames(df, var = "variable"))
  cl <- hclust(dist(mat), method = method)
  ord <- order.dendrogram(as.dendrogram(cl))
  factor(variable, unique(variable)[ord])
}
```

Heat map of fitness for *all genes and all conditions*.

```{r, fig.width = 8, fig.height = 2.2}
plot_heatmap_all <- df_gene %>% filter(time == 0, !is.na(locus)) %>%
  mutate(locus = fct_cluster(locus, condition, wmean_fitness)) %>%
  mutate(wmean_fitness = wmean_fitness %>% replace(., . > 4, 4) %>% replace(., . < -4, -4)) %>%
  ggplot(aes(x = locus, y = condition, fill = wmean_fitness)) +
  geom_tile() + custom_theme(legend.pos = "right") +
  labs(x = paste0("genes (", length(unique(df_gene$locus)),")"), y = "") +
  theme(axis.text.x = element_blank(), axis.ticks.x = element_blank()) +
  scale_fill_gradientn(colours = c(custom_colors[1], grey(0.9), custom_colors[2]),
    limits = c(-4, 4))

print(plot_heatmap_all)
save_plot(plot_heatmap_all, width = 8, height = 2.2)
```


Now we can plot _all_ genes, a subset with _only significant genes_, and a dendrogram for clustering. The result is hard to interpret. With some exceptions, most genes are grouped in broad unspecific clusters that do not reveal clear relationships between treatment variables and fitness outcome.


```{r, fig.width = 8, fig.height = 4}
# prepare new df and plot heatmap
df_heatmap <- df_gene %>% filter(time == 0, !is.na(locus)) %>%
  group_by(locus) %>% filter(any(!between(wmean_fitness, -4, 4))) %>% ungroup %>%
  mutate(locus = fct_cluster(locus, condition, wmean_fitness)) %>%
  mutate(wmean_fitness = wmean_fitness %>% replace(., . > 8, 8) %>% replace(., . < -8, -8))

plot_heatmap_sig <- df_heatmap %>%
  ggplot(aes(x = locus, y = condition, fill = wmean_fitness)) +
  geom_tile() + custom_theme(legend.pos = "right") +
  labs(x = paste0("genes (", length(unique(df_gene$locus)),")"), y = "") +
  theme(axis.text.x = element_blank(), axis.ticks.x = element_blank()) +
  scale_fill_gradientn(colours = c(custom_colors[1], grey(0.9), custom_colors[2]),
    limits = c(-8, 8))

# prepare dist object for clustering and plot dend
dist_heatmap <- df_heatmap %>% select(locus, condition, wmean_fitness) %>%
  pivot_wider(names_from = condition, values_from = wmean_fitness) %>%
  column_to_rownames(var = "locus") %>% as.matrix %>%
  dist

plot_cluster_dend <- dist_heatmap %>%
  hclust(method = "ward.D2") %>% as.dendrogram %>%
  set("branches_k_col", custom_colors[1:5], k = 5) %>%
  set("branches_lwd", 0.5) %>%
  as.ggdend %>%
  ggplot(labels = FALSE)

# arrange both on same plot
ggarrange(nrow = 2, heights =  c(0.5, 0.5),
  plot_cluster_dend + theme(plot.margin = unit(c(0.1, 0.09, -0.15, 0.136),"npc")),
  plot_heatmap_sig
)
```

## Gene similarity by dimensionality reduction methods

We use two different dimensionality reduction methods, **nMDS** and **t-SNE**. We can check if these methods reproduce the clustering for the significantly regulated genes produced with `hclust`. Analysis shows that the small clusters are more strongly separated from the rest.

```{r, fig.width = 8, fig.height = 4}
# set a seed to obtain same pattern for stochastic methods
set.seed(123)

# run nMDS analysis
NMDS <-  dist_heatmap %>% metaMDS
df_nmds <- NMDS$points %>% as_tibble(rownames = "locus") %>%
  left_join(enframe(name = "locus", value = "cluster",
    cutreeord(hclust(dist_heatmap, method = "ward.D2"), k = 5)))

# run t-SNE analysis
SNE <- dist_heatmap %>% tsne(max_iter = 500, perplexity = 8)
df_tsne <- SNE %>% setNames(c("x", "y")) %>% as_tibble %>%
  mutate(locus = unique(df_heatmap$locus)) %>%
  left_join(enframe(name = "locus", value = "cluster",
    cutreeord(hclust(dist_heatmap, method = "ward.D2"), k = 5)))

plot_nmds <- df_nmds %>%
  ggplot(aes(x = MDS1, y = MDS2, color = factor(cluster))) +
  geom_point(size = 2) + labs(title = "nMDS") +
  custom_theme(legend.position = c(0.85, 0.78)) +
  scale_color_manual(values = custom_colors)

plot_tsne <- df_tsne %>%
  ggplot(aes(x = V1, y = V2, color = factor(cluster))) +
  geom_point(size = 2) + labs(title = "t-SNE") +
  custom_theme(legend.position = c(0.85, 0.78)) +
  scale_color_manual(values = custom_colors)

ggarrange(ncol = 2, plot_nmds, plot_tsne)
ggsave("../figures/plot_nmds_tsne.svg",
  plot = ggarrange(ncol = 2, plot_nmds, plot_tsne),
  device = "svg", width = 8, height = 4)
```

## Fit multiple linear regression models

We can find clusters of genes with similar fitness, but it is also important to identify _why_ they cluster together. In order to find out _which variables_ determine the fitness outcome of a gene, we can perform **multiple linear regression**. Each gene needs to have fitness outcomes annotated with the different (mixed) variables `carbon`, `light`, `treatment`. The latter can be subdivided in individual treatment columns glucose, DCMU, fluctuating light, and so on. Multiple linear regression fits a linear model of the following form to the data:

`response ~ intercept + predictor A x slope A + predictor B x slope B x ...`

Here, `fitness` is the response variable, the different conditions are the predictors. It is important to convert the categorical predictors into (numerical) dummy variables. Then for each individual gene, multiple linear models are fitted and the power of each predictor variable to predict the response is extracted.

```{r}
# fixed model with 6 predictor variables -- dynamic layout would 
# be better in future
fit_linreg <- function(y, x1, x2, x3, x4, x5, x6){
  fit <- lm(y ~ x1 + x2 + x3 + x4 + x5 + x6)
  c(coefficients(fit), summary(fit)$coefficients[, 4],
    summary(fit)$r.squared)
}

# recode categorical to numerical (dummy) variables
df_linreg <- df_gene %>%
  filter(!is.na(locus)) %>%
  select(locus, carbon, light, treatment, wmean_fitness) %>% distinct %>%
  mutate(
    carbon = recode(carbon, `HC` = 1, `LC` = 0),
    light = recode(light, `LL` = 0, `IL` = 0.5, `HL` = 1)) %>%
  mutate(dummy = 1, treatment = replace(treatment, treatment == "", "-")) %>%
  pivot_wider(names_from = treatment, values_from = dummy, values_fill = 0) %>%
  mutate(`+G` = `+G` + `+D, +G`) %>% rename(`+D` = `+D, +G`) %>% select(-`-`) %>%
  # fit model
  group_by(locus) %>%
  summarize(coefficient = fit_linreg(wmean_fitness, carbon, light, `-N`, `+FL`, `+G`, `+D`),
    .groups = "keep") %>% #unnest(coefficient) %>%
  mutate(treatment = c(rep(c("intercept", "carbon", "light", "-N", "+FL", "+G", "+D"), 2) %>% 
    paste0(rep(c("", "pval_"), each = 7), .), "r_squared"))
```

Now we can overlay the information of the best predictor variable on the cluster map produced by tSNE, for example, and this way identify groups of genes regulated in a similar degree, by similar variables.

```{r, fig.width = 9, fig.height = 12}
plot_tsne_linreg <- df_tsne %>%
  inner_join(df_linreg, by = "locus") %>%
  left_join(select(df_gene, locus, sgRNA_target) %>% distinct, by = "locus") %>%
  filter(!str_detect(treatment, "intercept|pval|r_squared")) %>%
  mutate(sgRNA_target = if_else(abs(coefficient) > 2, sgRNA_target, "")) %>%
  mutate(point_size = abs(coefficient),
    coefficient = coefficient %>% replace(., . > 5, 5) %>% replace(., . < -5, -5)) %>%
  
  ggplot(aes(x = V1, y = V2, size = point_size,
    color = coefficient, label = sgRNA_target)) +
  geom_point() +
  labs(title = "t-SNE clustering of DF genes", 
    subtitle = paste0("dot color/size encodes effect of variable, n = ", nrow(df_tsne))) +
  custom_theme(aspect = 1) +
  scale_color_gradientn(limits = c(-5, 5),
    colours = c(custom_colors[1], grey(0.6, 0.8), custom_colors[2])) +
  scale_size_continuous(range = c(1, 6)) +
  geom_text_repel(size = 3, max.overlaps = 50) +
  facet_wrap( ~ treatment, ncol = 2)

print(plot_tsne_linreg)
```

This strategy reveals a list of interesting condition-specific genes:

- Nitrogen limitation: `ssr3532` - unknown short protein, strongest known interaction in STRING with GlsA glutaminase
- Fluctuating light:
  - `sll1521` - Putative diflavin flavoprotein A3 (dfa3), negatively corr. with fitness
  - `sll0217` Putative diflavin flavoprotein A2 (dfa2), positively corr. with fitness
- Mixotrophy:
  - `sll0593` - glk, glucokinase, catalyzes P-ylation of Glc to G6P
  - `sll1533` - pilT, fimbria assembly, mobility, Glc transport or sensing?
  - `ssl3364` - unknown short protein, strongly interacts with RbcX, RbcR, Prk. Important for C-metabolism adapation?
- Light:
  - `ssr2142` ycf19, short unknown protein, interacts with psbO and Tat membrane protein insertion system,
  - `slr0963` sir, sulfite reductase, ferredoxin H2O + HS + ferredoxin <-> H+ + reduced ferredoxin + sulfite,
    strongly interacts with other proteins in sulfur metabolism, specifically related to cofactor biosynthesis, 
    cobalamin (vitamin B12) and    siroheme
- Light, mixotrophy, heterotrophy: cluster of photosynthesis related genes increase fitness when KOed: apcA,D,E, psbB,C,D
- Carbon:
  - `sll0217` Putative diflavin flavoprotein A2 (dfa2), KO negatively correlated with fitness with C, positive with +FL
  - `sll0218` same behavior as dfa2, interacts with dfa2,4, contributes to PSII stabilization, 
     [Bersanini et al., 2017](https://pubmed.ncbi.nlm.nih.gov/27928824/)

## List of genes with strong fitness correlation

The table with linear regression coefficients and p-values is reshaped to long format for better readability. The `kableExtra` package is used to color cells for easier recognition. Then we subset the table for each treatment in order to spot the most interesting genes.

```{r}
df_linreg_wide <- df_linreg %>%
  pivot_wider(names_from = treatment, values_from = coefficient) %>%
  left_join(select(df_gene, locus, sgRNA_target) %>% distinct, by = "locus") %>%
  select(-matches("intercept")) %>%
  filter(if_any(matches("^(carb|light|\\-|\\+)"), ~ abs(.) > 2)) %>%
  mutate(across(matches("carb|light|\\-|\\+"), ~ round(., 3))) %>% 
  ungroup %>% select(sgRNA_target, locus, matches("."))

color_table <- function(df, variable) {
  filter(df, abs(.data[[variable]]) > 2) %>% 
  select(matches("^(sg|loc|r_s|carb|light|\\-|\\+)") | all_of(paste0("pval_", variable))) %>%
  arrange(desc(.data[[variable]])) %>%
  mutate(across(3:8, ~ cell_spec(., "html", color = "white",
      background = spec_color(., option = "E", scale = c(-5.5, 5.5)),
      bold = TRUE))) %>%
  kbl(format = "html", escape = F) %>%
  kable_paper("striped", full_width = F)
}
```


```{r}
df_linreg_wide %>% color_table("carbon")
```

```{r}
df_linreg_wide %>% color_table("light")
```

```{r}
df_linreg_wide %>% color_table("-N")
```

```{r}
df_linreg_wide %>% color_table("+FL")
```

```{r}
df_linreg_wide %>% color_table("+G")
```

```{r}
df_linreg_wide %>% color_table("+D")
```
Based on the multiple linear model correlations, we can try to extract a shortlist of the most interesting **hypothetical genes**. These could warrant further investigations.

```{r}
list_top_unknown_hits <- df_linreg_wide %>%
  left_join(df_uniprot, by = "locus") %>%
  # filter by name: only unknown proteins
  filter(
    is.na(gene_name_short),
    str_detect(protein, "[a-zA-Z]{3}[0-9]{4} protein|Uncharacterized")) %>%
  # filter by effect: only correlation > 3
  filter(if_any(matches("^(carb|light|\\-|\\+)"), ~ abs(.) > 3)) %>%
  arrange(desc(r_squared)) %>%
  pull(locus)

df_linreg_wide %>% filter(locus %in% list_top_unknown_hits) %>%
  select(!starts_with("pval"), -sgRNA_target) %>%
  mutate(across(2:7, ~ cell_spec(., "html", color = "white",
      background = spec_color(., option = "E", scale = c(-5.5, 5.5)),
      bold = TRUE))) %>%
  kbl(format = "html", escape = F) %>%
  kable_paper("striped", full_width = F)
```


## Extract and analyze interesting gene clusters

The list above shows the genes whose fitness is most significantly correlated with one of the treatments.
This list of genes is extracted and then simply fitness per condition is plotted as a heatmap, in order to confirm the trends from fitting the multiple liner regression models.


```{r, fig.width = 4, fig.height = 4}
plot_sgRNAs_light <- df_gene %>%
  filter(locus %in% list_top_unknown_hits, time == 0) %>%
  mutate(sgRNA_target = fct_cluster(sgRNA_target, condition, wmean_fitness)) %>%
  mutate(condition = fct_cluster(condition, sgRNA_target, wmean_fitness)) %>%
  mutate(wmean_fitness = wmean_fitness %>% replace(., . > 4, 4) %>% replace(., . < -4, -4)) %>%
  ggplot(aes(x = condition, y = sgRNA_target, fill = wmean_fitness)) +
  geom_tile() + custom_theme() +
  labs(title = "Top unknown genes", x = "", y = "") +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1)) +
  scale_fill_gradientn(colours = c(custom_colors[1], grey(0.9), custom_colors[2]),
    limits = c(-4, 4))

print(plot_sgRNAs_light)
save_plot(plot_sgRNAs_light, width = 8.0, height = 3.5)
```
**Summary**

- `sll0364` - 139 AA. KD has higher fitness in HC conditions and lower fitness in HL. Negatively regulating carbon metabolism?
- `sll0481` - 155 AA. KD has higher fitness in +G conditions and lower fitness in HL. Membrane localization. Negatively regulating glycolysis?
- `sll0877` - 456 AA. KD has higher fitness _only_ in HC,LL. Mitigates light limitation?
- `ssl3364` -  74 AA. KD has lower fitness on all HC/+G conditions. This protein is known as CP12 protein, regulating glycolytic flux at GAPDH and PRK.
- `ssr3532` -  80 AA. KD lower fitness on N-limitation and C-limitation (LC-HL combinations). Same operon as glutaminase glsA (slr2079, catalyzes deamination of gln --> glu), regulatory, involved in N metabolism?
- `slr1990` - 240 AA, 5 TM domains. KD higher fitness in photoheterotrophy, lower fitness in all HC/LL conditions. Something important for photosystems? Something that wastes e- in photoheterotrophic conditions?
- `sll6055` - 152 AA. Fitness profile as above. Multiubiquitin domain, involved in protein modification/degradation of PS proteins?
- `slr1505` - 198 AA. Fitness profile as above. No useful information.
- `sll1378` - 300 AA.  KD has lower fitness on all LL conditions. Membrane associated protein? In STRING, potential interaction with PbsA1 and PbsA2 (Heme oxygenase 1 and 2). Potentially important for chlorophyll or heme biosynthesis --> would explain importance for photosynthesis in LL condition.
- `slr1102` - 853 AA. KD has lower fitness on all LL conditions. 4 known domains, FHA (forkhead-associated domain is a phosphopeptide recognition domain found in many regulatory proteins), PAS (signaling, often involved in circadian proteins, detect their signal by way of an associated cofactor like heme, flavin), GGDEF (involved in signal transduction, likely to catalyze synthesis or hydrolysis of cyclic diguanylate c-diGMP), EAL (shown to stimulate degradation of a second messenger, cyclic di-GMP, candidate for a diguanylate phosphodiesterase function. Together with the GGDEF domain, EAL might be involved in regulating cell surface adhesiveness in bacteria). Source: InterPro. Embedded in a [tight network](https://string-db.org/network/1148.1651834) of interacting proteins all involved in chromophore biosynthesis/maturation.

**Apc and cpc repression mutants** encoding phycobilisomes are also enriched in high light


```{r, fig.width = 3.5, fig.height = 4.5}
plot_sgRNAs_phycobil <- df_gene %>%
  filter(str_detect(gene_name, "[ac]pc"), time == 0) %>%
  mutate(wmean_fitness = wmean_fitness %>% replace(., . > 4, 4) %>% replace(., . < -4, -4)) %>%
  ggplot(aes(x = condition, y = fct_rev(sgRNA_target), fill = wmean_fitness)) +
  geom_tile() + custom_theme() +
  labs(title = "Apc/Cpc repression mutants", x = "", y = "") +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1)) +
  scale_fill_gradientn(colours = c(custom_colors[1], grey(0.9), custom_colors[2]),
    limits = c(-4, 4))

print(plot_sgRNAs_phycobil)
save_plot(plot_sgRNAs_phycobil, width = 6.5, height = 3.5)
```


# Direct comparison of gene fitness

## Fitness of all conditions vs each other

We can plot selected conditions against each other and add gene labels in order to find or confirm particular patterns.

```{r}
make_fitness_plot <- function(data, vars, title = NULL) {
  # prepare data for two  variables each
  data %>% ungroup %>%
    filter(condition %in% vars, sgRNA_type == "gene") %>%
    select(locus, sgRNA_target, condition, wmean_fitness) %>% distinct %>%
    pivot_wider(names_from = condition, values_from = wmean_fitness) %>%
    mutate(
      dfit = get(vars[1]) - get(vars[2]),
      significant = !between(dfit, quantile(dfit, probs = c(0.003)),
        quantile(dfit, probs = c(0.997))),
      sgRNA_target = if_else(significant, sgRNA_target, "")) %>%
    
    # plot
    ggplot(aes(x = get(vars[1]), y = get(vars[2]), 
      color = significant, label = sgRNA_target)) +
    geom_point(size = 1) + custom_theme(legend.position = 0) +
    geom_abline(intercept = 0, slope = 1, col = grey(0.5), lty = 2, size = 0.8) +
    geom_abline(intercept = 4, slope = 1, col = grey(0.5), lty = 2, size = 0.8) +
    geom_abline(intercept = -4, slope = 1, col = grey(0.5), lty = 2, size = 0.8) +
    geom_text_repel(size = 3, max.overlaps = 50) +
    labs(title = title, x = vars[1], y = vars[2]) +
    coord_cartesian(xlim = c(-9, 5), ylim = c(-9, 5)) +
    scale_color_manual(values = c(grey(0.5), custom_colors[2]))
}

# browse through all possible condition combinations;
# we need a helper function that detects duplicated combinations
duplicated_2vec <- function(x, y) {
  xy = paste(x, y); yx = paste(y, x)
  sapply(xy, function(xval) {
    which(xval == yx) <= which(xval == xy)
  })
}

list_condition_pairs <- lapply(
  unique(df_gene$condition) %>% expand_grid(x = ., y = .) %>%
    filter(!duplicated_2vec(x, y)) %>% t %>% as.data.frame %>% as.list,
  function(var) {
    make_fitness_plot(df_gene, vars = var,
      title = paste(var, collapse = "  -  "))
  }
)

# export images
invisible(capture.output(
  lapply(list_condition_pairs, function(pl) {
    pl_name <- paste0("../figures/pairwise_comparisons/plot_", pl$labels$x, "_", pl$labels$y, ".png")
    png(filename = pl_name, width = 800, height = 800, res = 120)
    print(pl)
    dev.off()
  })
))
```


```{r, fig.width = 5, fig.height = 5}
# example of first 4 combinations
list_condition_pairs[1:4]
```

# Differential fitness of selected gene sets

## Central carbon metabolism

To plot gene fitness for the enzymes of central carbon metabolism, we use the complete list of enzymes and the genes that they are mapped to (obtained from KEGG). We can extract gene sets for specific pathways and plot fitness. We start with glycolysis and Calvin cycle enzymes.

```{r}
list_central_met_pathways <- c(
  "Glycolysis / Gluconeogenesis",
  "Pentose phosphate pathway",
  "Carbon fixation in photosynthetic organisms",
  "Photosynthesis",
  "Citrate cycle (TCA cycle)",
  "Pyruvate metabolism",
  "Glyoxylate and dicarboxylate metabolism"
)
```


```{r}
plot_gene_fitness <- function(df, pw = NULL, gene = NULL,
  title = NULL, ncol = 8, legend.position = "bottom") {
  df <- df %>% filter(time == 0)
  if (!is.null(pw)) {
    df <- df %>% inner_join(df_kegg %>% filter(kegg_pathway == pw) %>% select(locus),
      by = "locus")
    title <- pw
  } else if (!is.null(gene)) {
    df <- df %>% filter(locus %in% gene)
  }
  
  ggplot(df, aes(x = condition, y = wmean_fitness, 
    ymin = wmean_fitness-sd_fitness, 
    ymax = wmean_fitness+sd_fitness, fill = condition, color = condition)) +
    geom_col(position = "dodge", width = 0.6) +
    geom_errorbar(position = "dodge", width = 0.6, size = 1) +
    custom_theme(aspect.ratio = 1,
      legend.position = legend.position, legend.key.size = unit(0.4, "cm")) + 
    labs(title = title, x = "", y = "fitness") +
    theme(axis.text.x = element_blank(), axis.ticks = element_blank()) +
    scale_fill_manual(values = colorRampPalette(custom_colors[1:5])(11)) +
    scale_color_manual(values = colorRampPalette(custom_colors[1:5])(11)) +
    facet_wrap(~ sgRNA_target, ncol = ncol, drop = FALSE)
}
```


```{r, fig.width = 8, fig.height = 6}
print(plot_gene_fitness(df_gene, pw = list_central_met_pathways[[1]]))
ggsave("../figures/plot_fitness_glycolysis.svg",
  plot_gene_fitness(df_gene, pw = list_central_met_pathways[[1]]),
  width = 8, height = 6)
```

```{r, fig.width = 8, fig.height = 5}
print(plot_gene_fitness(df_gene, pw = list_central_met_pathways[[2]]))
ggsave("../figures/plot_fitness_pentose.svg",
  plot_gene_fitness(df_gene, pw = list_central_met_pathways[[2]]),
  width = 8, height = 5)
```

```{r, fig.width = 8, fig.height = 5}
print(plot_gene_fitness(df_gene, pw = list_central_met_pathways[[3]]))
ggsave("../figures/plot_fitness_carbonfix.svg",
  plot_gene_fitness(df_gene, pw = list_central_met_pathways[[3]]),
  width = 8, height = 5)
```


```{r, fig.width = 8, fig.height = 4}
print(plot_gene_fitness(df_gene, pw = list_central_met_pathways[[5]]))
ggsave("../figures/plot_fitness_citrate.svg",
  plot_gene_fitness(df_gene, pw = list_central_met_pathways[[5]]),
  width = 8, height = 4)
```

## Gene fitness in mixotrophy and heterotrophy

Using [fluctuator](https://github.com/m-jahn/fluctuator), we can import a custom metabolic map for _Synechocystis_ sp. PCC 6803, and overlay published fluxes that were measured with LC-MS using isotopically labelled carbon sources ([Nakajima et al., 2014](https://doi.org/10.1093/pcp/pcu091)).

Fluctuator can be installed using a function from `devtools`:

```{r, eval = FALSE}
devtools::install_github("m-jahn/fluctuator")
```

We import the metabolic flux data from the supplemental items of [Nakajima et al., 2014](https://doi.org/10.1093/pcp/pcu091).

```{r}
library(fluctuator)

# import flux data
df_nakajima_mfa <- read.csv("../data/input/Nakajima2014_metabolic_fluxes.csv")

# generate stroke width and color
df_nakajima_mfa <- df_nakajima_mfa %>%
  mutate(
    stroke_width = 0.3 + (0.7*sqrt(abs(flux))),
    stroke_color = abs(flux) %>% {1+(./max(.))*9} %>% round,
    stroke_color_rgb =  colorRampPalette(custom_colors[c(5,2,1)])(10)[stroke_color])
```

The next step is to overlay the fluxes. We generate two types of maps, mixotrophy and photoheterotrophy.
The stroke width and color for all reactions is set by the flux magnitude.

```{r}
for (cond in c("mixotroph", "photoheterotroph")) {
  # import map 
  SVG_template <- read_svg("../data/input/map_central_metabolism_syn.svg")
  
  # set stroke on SVG map
  SVG_mix <- set_attributes(SVG_template,
    node = filter(df_nakajima_mfa, condition == cond)$reaction,
    attr = "style",
    pattern = "stroke-width:[0-9]+\\.[0-9]+",
    replacement = paste0("stroke-width:",
      filter(df_nakajima_mfa, condition == cond)$stroke_width))
  
  # set color
  SVG_mix <- set_attributes(SVG_mix,
    node = filter(df_nakajima_mfa, condition == cond)$reaction,
    attr = "style",
    pattern = "stroke:#b3b3b3",
    replacement = paste0("stroke:",
      filter(df_nakajima_mfa, condition == cond)$stroke_color_rgb))
  
  # set arrow directionality
  SVG_mix <- set_attributes(SVG_mix,
    node = filter(df_nakajima_mfa, condition == cond, flux < 0)$reaction,
    attr = "style",
    pattern = "marker-end:url\\(#marker[0-9]*\\);",
    replacement = "")
  
  SVG_mix <- set_attributes(SVG_mix,
    node = filter(df_nakajima_mfa, condition == cond, flux > 0)$reaction,
    attr = "style",
    pattern = "marker-start:url\\(#marker[0-9]*\\);",
    replacement = "")
  
  write_svg(SVG_mix, file = paste0("../data/output/map_", cond, "y.svg"))
}
```

Metabolic flux with mixotrophy |  Metabolic flux with photoheterotrophy
:-------------------------:|:-------------------------:
![](../data/output/map_mixotrophy.svg)  |  ![](../data/output/map_photoheterotrophy.svg)


Now we plot fitness of central carbon metabolism genes for two or three selected conditions. These will be added to the metabolic map manually. The mixotrophic conditions `LC, LL, +G` and `HC, LL, +G` turned out to be very similar.

```{r}
df_centralcarb <- tibble(
  locus = c(   "sll0593", "slr0329", "slr1843", "sll1479", "sll0329", "slr1349",
    "slr0952", "slr2094", "slr0943", "sll0018", "slr0783", "sll1342", "slr0884",
    "slr0394", "slr1945", "slr0752", "sll0587", "sll1275", "sll1070", "slr1793",
    "slr0194", "ssl2153", "sll0807", "sll1525", "slr0009", "slr0012", "sll1721",
    "sll1841", "slr1096", "slr1934", "sll0401", "slr0665", "slr1289", "slr1096",
    "sll1023", "sll1557", "sll0823", "sll1625", "slr0201", "slr1233", "slr0018",
    "sll0891", "sll0920", "slr0721"),
  reaction = c("HEX", "HEX", "G6PDH", "PGL", "GND", "PGI", "FBP",
    "FBP", "FBA", "FBA", "TPI", "GAPDH", "GAPDH", "PGK", "PGM", "ENO",
    "PYK", "PYK", "TKT", "TAL", "RPI", "RPI", "RPE", "PRUK", "RUBISCO",
    "RUBISCO", "PDH", "PDH", "PDH", "PDH", "CS", "ACONT", "ICDH", "AKGDH",
    "SUCOAS", "SUCOAS", "SUCD", "SUCD", "SUCD", "SUCD", "FUM", "MDH",
    "PPC", "ME")) %>%
  inner_join(df_kegg) %>% group_by(locus) %>% slice(1) %>%
  ungroup %>% arrange(reaction)
```



```{r, fig.width = 6, fig.height = 5}
plot_centralcarb_minifig <- df_gene %>% filter(
    time == 0,
    condition %in% c("LC, LL", "LC, LL, +G", "LC, LL, +D, +G")) %>%
  inner_join(df_centralcarb, by = "locus") %>%
  mutate(sgRNA_target = paste0(reaction, " (", sgRNA_target, ")")) %>%
  mutate(condition = factor(condition, c("LC, LL", "LC, LL, +G", "LC, LL, +D, +G"))) %>%
  
  ggplot(aes(x = condition, y = wmean_fitness, 
    ymin = wmean_fitness-sd_fitness, 
    ymax = wmean_fitness+sd_fitness, fill = condition, color = condition)) +
  geom_hline(yintercept = c(0, -5, -10), linetype = 3, col = grey(0.6)) +
  geom_col(position = "dodge", width = 0.6) +
  geom_errorbar(position = "dodge", width = 0.6, size = 1) +
  custom_theme(aspect.ratio = 1,
    legend.position = "bottom", legend.key.size = unit(0.4, "cm")) + 
  theme(axis.text.x = element_blank(), axis.text.y = element_blank(), 
    axis.ticks = element_blank(), panel.grid.major = element_blank(),
    strip.text = element_text(size = 8)) +
  labs(x = "", y = "") +
  coord_cartesian(ylim = c(-11, 1)) +
  scale_fill_manual(values = custom_colors[c(5,2,3)]) +
  scale_color_manual(values = custom_colors[c(5,2,3)]) +
  facet_wrap(~ sgRNA_target, ncol = 9, drop = FALSE)

plot_centralcarb_minifig
```

```{r, include = FALSE}
svg(filename = "../figures/plot_centralcarb_minifig.svg", width = 6, height = 5)
plot_centralcarb_minifig
dev.off()
```

Similar but more concisely, we can test if there is a correaltion between flux and fitness penalty upon repression. Theoretically, such a correlation should exist because repression of high flux enzymes should have the strongest penalty on fitness. However, examination of the data does not reveal a clear correlation. Causes for this may be manifold, including the compensation of gene duplicates/iso-enzymes for gene knock down at high flux reactions.

```{r}
df_gene %>% filter(
    time == 0,
    condition %in% c("LC, LL, +G", "LC, LL, +D, +G")) %>%
  mutate(condition = recode(condition, 
    `LC, LL, +G` = "mixotroph", `LC, LL, +D, +G` = "photoheterotroph")) %>%
  inner_join(df_centralcarb, by = "locus") %>%
  select(sgRNA_target, locus, gene_name, condition, wmean_fitness, sd_fitness, reaction) %>%
  inner_join(by = c("condition", "reaction"),
    select(df_nakajima_mfa, condition, reaction, flux, ci_low, ci_high)) %>%
  
  ggplot(aes(x = abs(flux), y = abs(wmean_fitness),
    ymin = abs(wmean_fitness)-sd_fitness, 
    ymax = abs(wmean_fitness)+sd_fitness,
    xmin = abs(ci_low), 
    xmax = abs(ci_high))) +
  geom_errorbar(orientation = "x", col = grey(0.75)) +
  geom_errorbar(orientation = "y", col = grey(0.75)) +
  geom_point() +
  coord_cartesian(xlim = c(-0.2, 3.2), ylim = c(-0.5, 7.5)) +
  custom_theme(aspect.ratio = 1) + 
  facet_wrap(~ condition, ncol = 2)
```



## Adaptation to light and carbon excess

We will look at three different types of regulatory adaptations:

- `apc`/`cpc`antenna proteins (phycobilisomes), known to be among the most expressed and regulated genes in cyanos
- flavoproteins Flv1 (`sll1521`), Flv2 (`sll0219`), Flv3 (`sll0550`), Flv4 (`sll0217`), `sll0218` (in flv2/4 operon)
- low affinity/high flux transporters Ci transporters: bicA (`sll0834`), NDH-I4 with ndhF4, D4, cupB (`sll0026`, `sll0027`, `slr1302`)
- high affinity/low flux inducible Ci transporters: BCT1/cmpAB(porB)CD (`slr0040-44`), SbtA/B (`slr1512`, `slr1513`), NDH-I3 with ndhF3, ndhD3, cupA, cupS (`sll1732-35`)
- carbon transport regulatory proteins: ccmR/rbcR (`sll1594`), cmpR (`sll0030`), cyabrB1 (`sll0359`), cyabrB2 (`sll0822`)


```{r}
plot_phycobilisome <- df_gene %>% filter(str_detect(gene_name, "[ac]pc[ABCDEFG]")) %>%
  plot_gene_fitness(ncol = 6, legend.position = 0)

plot_flv_genes <- df_gene %>% filter(locus %in% c("sll1521", "sll0219", "sll0550", "sll0217", "sll0218")) %>%
  mutate(sgRNA_target = recode(sgRNA_target, `sll1521` = "Flv1 (sll1521)", `sll0219` = "Flv2 (sll0219)",
    `sll0550` = "Flv3 (sll0550)", `sll0217` = "Flv4 (sll0217)")) %>%
  mutate(sgRNA_target = factor(sgRNA_target, c(unique(sgRNA_target), ""))) %>%
  plot_gene_fitness(ncol = 6, legend.position = 0)

plot_carbon_uptake <- df_gene %>% filter(locus %in% c(
    "sll0026", "sll0027", "slr1302",
    "sll1732", "sll1733", "sll1734", "sll1735", "slr0040", "slr0041","slr0043","slr0044"
  )) %>%
  mutate(sgRNA_target = recode(sgRNA_target,
    `nrtC2` = "cmpC", `nrtD3` = "cmpD",
    `sll1734` = "cupA", `slr1302` = "cupB",
    `sll1735` = "cupS", `ndhF2` = "ndhF3"
  )) %>%
  mutate(sgRNA_target = factor(sgRNA_target, unique(sgRNA_target)[c(4,6,11,3,5,9,10,1,2,7,8)])) %>%
  plot_gene_fitness(ncol = 6, legend.position = 0) +
  coord_cartesian(ylim = c(-7.9, 2.4))
```

Figure 3 draft:

```{r, fig.width = 6.0, fig.height = 8.5}
ggarrange(nrow = 3, heights =  c(0.47, 0.2, 0.33), labels = LETTERS[1:3], font.label = list_fontpars,
  plot_phycobilisome,
  plot_flv_genes,
  plot_carbon_uptake
)
```

```{r, include = FALSE}
svg(filename = "../figures/figure3.svg", width = 6.0, height = 8.5)
ggarrange(nrow = 3, heights =  c(0.47, 0.2, 0.33), labels = LETTERS[1:3], font.label = list_fontpars,
  plot_phycobilisome,
  plot_flv_genes,
  plot_carbon_uptake
)
dev.off()
```

As a Supplementary figure to C), we can **plot all other carbon transporters and regulatory genes** that showed a less remarkable effect.

```{r, fig.width = 6,  fig.height = 2.75}
plot_carbon_uptake_2 <- df_gene %>% filter(locus %in% c(
    "sll0834", "slr1512", "slr1513", "sll1594", "sll0030", "sll0359", "sll0822"
  )) %>%
  mutate(sgRNA_target = recode(sgRNA_target,
    `sll0834` = "bicA", `slr1512` = "sbtA", `slr1513` = "sbtB",
    `sll0359` = "cyabrB1", `sll0822` = "cyabrB2", `rbcR` = "ccmR"
  )) %>%
  mutate(sgRNA_target = factor(sgRNA_target, unique(sgRNA_target))) %>%
  plot_gene_fitness(ncol = 4, legend.position = "right")

plot_carbon_uptake_2
```

```{r, include = FALSE}
svg(filename = "../figures/plot_carbon_uptake.svg", width = 6.0, height = 2.75)
print(plot_carbon_uptake_2)
dev.off()
```


As another Supplementary Figure, we can plot the **total protein mass of the phycobilisome** determined by protein mass spectrometry.
This data was published in our study [Jahn et al., Cell Reports, 2018](https://www.cell.com/cell-reports/fulltext/S2211-1247(18)31485-2). The data can be downloaded directly from the ShinyProt github page where it is included for on demand visualization.

```{r, fig.width = 6, fig.height = 3.6}
load(url("https://github.com/m-jahn/ShinyProt/blob/master/data/Jahn_2018_Light_and_CO2_lim.Rdata?raw=true"))

plot_protmass_phycobilisome1 <- Jahn_2018_Light_and_CO2_lim %>%
  filter(str_detect(protein, "[ac]pc[ABCDEFG]"), sample != "CO2") %>%
  mutate(protein = str_extract(protein, "[ac]pc[ABCDEFG][12]?")) %>%
  ggplot(aes(x = factor(light), y = 100*mean_mass_fraction_norm, 
  fill = str_sub(protein, 1, 3), label = protein)) +
  lims(y = c(0, 22)) +
  geom_col(position = "stack", width = 0.7, col = grey(1), size = 0.2) +
  geom_text(size = 2.5, position = position_stack(vjust = 0.5), color = "white") +
  custom_theme(legend.position = "bottom", legend.key.size = unit(0.5, "cm")) +
  labs(title = "Light limitation", x = "µmol photons m^-2 s^-1", y = "% protein mass") +
  scale_fill_manual(values = custom_colors[c(2,4)]) +
  scale_color_manual(values = custom_colors[c(2,4)])

plot_protmass_phycobilisome2 <- Jahn_2018_Light_and_CO2_lim %>%
  filter(str_detect(protein, "[ac]pc[ABCDEFG]"), sample == "CO2") %>%
  mutate(protein = str_extract(protein, "[ac]pc[ABCDEFG][12]?")) %>%
  ggplot(aes(x = factor(co2_concentration), y = 100*mean_mass_fraction_norm, 
  fill = str_sub(protein, 1, 3), label = protein)) +
  lims(y = c(0, 22)) +
  geom_col(position = "stack", width = 0.7, col = grey(1), size = 0.2) +
  geom_text(size = 2.5, position = position_stack(vjust = 0.5), color = "white") +
  custom_theme(legend.position = "bottom", legend.key.size = unit(0.5, "cm")) +
  labs(title = "CO2 limitation", x = "% CO2 in air", y = "% protein mass") +
  scale_fill_manual(values = custom_colors[c(2,4)]) +
  scale_color_manual(values = custom_colors[c(2,4)])

ggarrange(ncol = 2, widths = c(0.5,0.5),
  labels = LETTERS[1:2], font.label = list_fontpars,
  plot_protmass_phycobilisome1,
  plot_protmass_phycobilisome2
)
```

```{r, include = FALSE}
svg("../figures/plot_protmass_phycobilisome.svg", width = 6, height = 3.6)
ggarrange(ncol = 2, widths = c(0.5,0.5),
  labels = LETTERS[1:2], font.label = list_fontpars,
  plot_protmass_phycobilisome1,
  plot_protmass_phycobilisome2
)
dev.off()
```

Other genes of interest that either did not show any (remarkable) effect on fitness, or do not meet the scope of this section:

- OCP (`slr1963`), pgr5 (`ssr2016`)
- SigB (`sll0306`), SigC (`sll0184`), SigD (`sll2012`), SigE (`sll1689`) (rpoD genes 1-4)
- ccmM (`sll1031`), ccmK2 (`sll1028`), ccmK1 (`sll1029`), ccmN (`sll1032`), ccmO (`slr0436`),
  ccmL (`sll1030`)
- CP12 (`ssl3364`)


## Genes where knock down leads to increased fitness


```{r, fig.width = 8, fig.height = 8}
list_genes_pos_fitness <- df_gene %>%
  filter(time == 0, !is.na(locus), wmean_fitness > 2) %>%
  pull(locus) %>% unique

plot_gene_fitness(df_gene, gene = list_genes_pos_fitness, title = "Genes with increased fitness (f > 2)")
ggsave("../figures/plot_fitness_increased.svg",
  plot_gene_fitness(df_gene, gene = list_genes_pos_fitness, title = "Genes with increased fitness (f > 2)"),
  width = 8, height = 8)
```

Summary:
- pmgA is once again the gene with strongest and most widespread fitness increase, validating results from library V1
- slr1916 same phenotype as pmgA just weaker. We also know this one from before. Must have identical role as pmgA.
- all PSII genes show increased fitness in photoheterotrophic condition --> PS is a burden here
- sll0689, pxcA, slr1609 - all increased fitness in HC,HL, first two are Na+/CO2 (?) trnasporters,
  slr1609 we know from before,   annotated as fatty acid CoA ligase, but probably it's something different
- sll6055, slr1505, slr1990 - all increased fitness in photoheterotrophic condition, and decreased fitness in HC/LL conditions.
  Not much is known about these genes, probably a role in photosynthesis, as the pattern is similar to psb genes (PSII maturation?)
- slr0813, slr0907, slr909, slr1299 - all increased fitness in HC/LL. Not clear what connects these genes functionally.


# Differential fitness of non-coding RNAs (ncRNAs)

## General trends

The first task to study ncRNAs is to generate a new data frame with additional annotation for ncRNAs.
Additional annotation tables were exported from Geneious and are based on the publication from [Mitschke et al., PNAS, 2010](https://doi.org/10.1073/pnas.1015154108). According to this publication, ncRNAs are grouped into four different (slightly overlapping) classes:

- non-coding regulatory RNAs (ncRNAs in strict sense) not associated to a gene
- iTSS, internal TSS within a gene
- asRNA, regulatory anti-sense RNAs associated with a gene
- Not included: 5'UTRs, alternative transcription start sites (TSS) associated to a gene

```{r}
df_ncRNA <- df_main %>% filter(sgRNA_type == "ncRNA") %>%
  # obtain number of sgRNAs per target
  group_by(sgRNA_target) %>%
  summarize(sgRNA_number = length(unique(sgRNA_position))) %>%
  # merge with df_gene table
  inner_join(df_gene, by = "sgRNA_target") %>%
  # generate ncRNA type based on target name
  select(-locus, -gene_name, -sgRNA_type) %>%
  mutate(sgRNA_target = str_sub(sgRNA_target, 4, 1000)) %>%
  left_join(by = "sgRNA_target",
    bind_rows(lapply(c("NC_000911_ncRNA.tsv", "NC_000911_asRNA.tsv", "NC_000911_iTSS.tsv"),
      FUN = function(f) read_tsv(paste0("../data/input/", f), col_types = cols())
    ))
  )

df_ncRNA %>% group_by(ncRNA_type) %>%
  filter(time == 0, condition == "HC, HL") %>%
  select(sgRNA_target, score) %>%
  summarize(n_targets = length(unique(sgRNA_target)), sum(score >= 4), sum(score < 4),
    percent = sum(score >= 4)/n()*100)
```

Looking at the fitness and significance scores for one conditions, it seems as internal transcription start sites are overrepresented in the group that shows an effect. This is not a surprise, given that sgRNAs targeting iTSS basically also repress the native gene as a regular sgRNA. We therefore exlcude iTSS from the analysis.

The library contains 1712 ncRNAs each targeted by 1 to 5 sgRNAs. Only very few of those showed an effect on fitness.
We can filter all ncRNAs that have a "significance" equivalent to a fitness score abs(F) >= 2 and -log10 p-value >= 2 (alpha = 0.01).
Significance here means effect size (F) multiplied by -log10 p-value, the threshold is indicated by the dashed line.

```{r, fig.width = 2.5, fig.height = 6}
plot_ncRNA_overview <- df_ncRNA %>% filter(time == 0, condition == "HC, HL") %>%
  mutate(ncRNA_type = factor(ncRNA_type, c("asRNA", "ncRNA", "iTSS"))) %>%
  ggplot(aes(x = wmean_fitness, y = -log10(p_value_adj), color = ncRNA_type)) +
  geom_point(alpha = 0.5, size = 1.5) +
  geom_line(data = data.frame(x = c(seq(-8, -0.5, 0.1), seq(0.5, 8, 0.1)),
    y = 4/c(seq(8, 0.5, -0.1), seq(0.5, 8, 0.1))),
    aes(x = x, y = y, shape = NULL, col = NULL), lty = 2) +
  coord_cartesian(xlim = c(-7, 7), ylim = c(0, 4)) +
  custom_theme(aspect = 1, legend.position = "none") +
  facet_wrap(~ ncRNA_type, ncol = 1) +
  labs(x = "fitness", y = expression("-log"[10]*" p-value")) +
  scale_color_manual(values = custom_colors)

print(plot_ncRNA_overview)
```
## Antisense RNAs as regulatory elements

The first part of a more detailed analysis is to extract asRNAs with differential fitness, and compare them to their associated genes. The assumption is that sgRNAs targeting asRNAs in reality repress transcription of their parent genes, and by these means produce a fitness effect that can not be attributed to the action of the asRNA itself. The first step is filter the ncRNA dataset and order ncRNAs by fitness similarity.

```{r, fig.width = 3.5, fig.height = 8}
df_ncRNA_select <- df_ncRNA %>%
  filter(ncRNA_type != "iTSS", time == 0) %>%
  group_by(sgRNA_target) %>%
  filter(any(score >= 4)) %>% ungroup %>%
  mutate(sgRNA_target = fct_cluster(sgRNA_target, condition, wmean_fitness))
```


```{r}
plot_asRNA_heat <- df_ncRNA_select %>% filter(ncRNA_type == "asRNA") %>%
  mutate(wmean_fitness = wmean_fitness %>% replace(., . > 4, 4) %>% replace(., . < -4, -4)) %>%
  ggplot(aes(x = condition, y = sgRNA_target, fill = wmean_fitness)) +
  geom_tile() + custom_theme(legend.pos = "bottom") +
  labs(x = "", y = "") +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1)) +
  scale_fill_gradientn(colours = c(custom_colors[1], grey(0.9), custom_colors[2]),
    limits = c(-4, 4))

# check correlation of asRNA fitness with associated gene fitness
plot_asRNA_xy <- df_ncRNA_select %>% filter(ncRNA_type == "asRNA") %>%
  left_join(by = c("condition", "locus"),
    select(df_gene, locus, condition, wmean_fitness, sd_fitness) %>% distinct %>%
    rename(gene_fitness = wmean_fitness, sd_gene_fitness = sd_fitness)) %>%
  select(locus, condition, wmean_fitness, gene_fitness) %>%
  mutate(locus = if_else(locus %in% c("slr0882", "sll1773", "sml0004", "slr1609", "slr1939"), locus, "other")) %>%
  ggplot(aes(x = wmean_fitness, y = gene_fitness, color = locus)) +
  geom_abline(intercept = 0, slope = 1, lty = 2) +
  geom_abline(intercept = 4, slope = 1, lty = 2) +
  geom_abline(intercept = -4, slope = 1, lty = 2) +
  geom_point() +
  coord_cartesian(xlim = c(-9, 5), ylim = c(-9, 5)) +
  custom_theme(legend.pos = c(0.15, 0.8), legend.key.size = unit(0.3, "cm")) +
  labs(x = "asRNA fitness", y = "gene fitness") +
  scale_color_manual(values = custom_colors[c(5,1:4,6)])
```

## noncoding RNAs as regulatory elements

The second part of this analysis is to look at non-gene associated (intergenic) ncRNA elements. Of these, several are known to have a regulatory effect.

```{r, fig.width = 8, fig.height = 7.5}
plot_ncRNA_heat <- df_ncRNA_select %>% filter(ncRNA_type == "ncRNA") %>%
  mutate(wmean_fitness = wmean_fitness %>% replace(., . > 4, 4) %>% replace(., . < -4, -4)) %>%
  ggplot(aes(x = condition, y = sgRNA_target, fill = wmean_fitness)) +
  geom_tile() + custom_theme(legend.pos = "none") +
  labs(x = "", y = "") +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1)) +
  scale_fill_gradientn(colours = c(custom_colors[1], grey(0.9), custom_colors[2]),
    limits = c(-4, 4))

ggarrange(ncol = 3,
  plot_ncRNA_overview,
  ggarrange(nrow = 2, heights = c(0.65, 0.35),
    plot_asRNA_heat, plot_asRNA_xy + theme(plot.margin = unit(c(4,12,16,12), "points"))),
  ggarrange(nrow = 2, heights = c(0.75, 0.25), plot_ncRNA_heat, ggplot() + custom_theme())
)
```

# Export summary table of all genes and conditions

Export a summary table of all genes and conditions, so that it's easy for other people to look up single conditions as for example done in [one-by-one fitness comparisons](#fitness-of-all-conditions-vs-each-other). This is best done in wide format (one column per condition).

```{r}
df_gene %>% ungroup %>%
  filter(sgRNA_type == "gene") %>%
  select(locus, sgRNA_target, gene_name, condition, wmean_fitness) %>% 
  distinct %>%
  pivot_wider(names_from = condition, values_from = wmean_fitness) %>%
  write_csv("../data/output/fitness_summary.csv")

df_gene %>%
  filter(sgRNA_type == "gene") %>%
  write_csv("../data/output/fitness_genes.csv")

df_kegg %>% write_csv("../data/output/kegg_annotation.csv")
```

The entire pipeline takes about 25 minutes to run on a standard notebook.
To work on single sections, the work space is exported to avoid constant recalculation of result tables.

```{r}
# remove large intermediate objects
rm("list_condition_pairs")
save(list = ls(), file = "../pipeline/CRISPRi_V2_data_processing.RData")
```


# Session Info

```{r}
sessionInfo()
```

